Patent Publication Number: US-2023153490-A1

Title: Systems and methods of anomaly detection for building components

Description:
BACKGROUND 
     The present disclosure relates generally to predicting faults or other anomalies for building components, such as heating, ventilation, and/or air conditioning (HVAC) components. In some implementations, the present disclosure relates more particularly to predicting building component (e.g., chiller) faults using models trained, for example, with machine learning (e.g., deep learning). 
     Chillers are often found in buildings and are components of HVAC systems. Chillers are subject to faults, which can cause unplanned shutdowns due to safety and other concerns. More specifically, chiller shutdowns may cause loss of efficiency, as well as damage to other expensive HVAC equipment during a shutdown. It is desirable to predict chiller shutdowns prior to shutdowns occurring. 
     Chiller faults are often unexpected and difficult to predict. Various factors may cause a chiller fault including overuse, required maintenance, safety concerns and environmental conditions, among other possible factors. With many factors capable of influencing sudden chiller faults, predicting future chiller failure is challenging. 
     SUMMARY 
     One implementation of the present disclosure is a method for generating a reliability model, comprising receiving, by a processing circuit, historical operating data associated with one or more chillers or chiller components, the historical operating data including two or more event dates associated with the one or more chillers, calculating, by the processing circuit, a runtime of a chiller of the one or more chillers based on the two or more event dates, calibrating, by the processing circuit, the runtime by determining an idle time associated with the chiller corresponding to a location of the chiller and performing an operation using the runtime and the idle time to generate a calibrated runtime, and training, by the processing circuit, a chiller reliability model using the calibrated runtime to produce a trained model. 
     In some embodiments, performing the operation includes subtracting the idle time from the runtime to generate the calibrated runtime. In some embodiments, training the chiller reliability model includes training at least one of (i) a Weibull model or (ii) a Cox model using the calibrated runtime to produce the trained model. In some embodiments, the method further comprises generating, by the processing circuit, a reliability metric describing a mean time between failures (MTBF) associated with the chiller based on the trained model. In some embodiments, the two or more event dates include a failure date associated with a failure of the chiller and a start date associated with a day when the chiller came online, and wherein calculating the runtime of the chiller includes determining an amount of time between the failure date and the start date. In some embodiments, the method further comprises receiving, by the processing circuit, warranty claim data associated with one or more warranty claims associated with the one or more chillers or chiller components, and parsing, by the processing circuit, the warranty claim data to identify the historical operating data by generating the start date associated with the chiller based on at least one of (i) a shipping date associated with a day when the chiller was shipped to a location of operation or (ii) a manufacture date associated with when the chiller was manufactured. 
     In some embodiments, the method further comprises parsing, by the processing circuit, the historical operating data to identify an element in the historical operating data having at least one of (i) a runtime that is below a threshold runtime, (ii) an event date that is before a threshold event date, or (iii) a failure type that is included in a list of failure types that are below a threshold number of failures, and trimming, by the processing circuit, the element from the historical operating data in response. In some embodiments, training the chiller reliability model to produce the trained model includes determining a shape parameter and a scale parameter of a Weibull model. 
     Another implementation of the present disclosure is one or more non-transitory computer-readable storage mediums having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to receive historical operating data associated with one or more chillers or chiller components, the historical operating data including two or more event dates associated with the one or more chillers, calculate a runtime of a chiller of the one or more chillers based on the two or more event dates, calibrate the runtime by (i) determining an idle time associated with the chiller corresponding to a location of the chiller and (ii) performing an operation using the runtime and the idle time to generate a calibrated runtime, and train a chiller reliability model using the calibrated runtime to produce a trained model. 
     In some embodiments, performing the operation includes subtracting the idle time from the runtime to generate the calibrated runtime. In some embodiments, training the chiller reliability model includes training at least one of (i) a Weibull model or (ii) a Cox model using the calibrated runtime to produce the trained model. In some embodiments, the instructions further cause the one or more processors to generate a reliability metric describing a mean time between failures (MTBF) associated with the chiller based on the trained model. In some embodiments, the two or more event dates include a failure date associated with a failure of the chiller and a start date associated with a day when the chiller came online, and wherein calculating the runtime of the chiller includes determining an amount of time between the failure date and the start date. In some embodiments, the instructions further cause the one or more processors to receive warranty claim data associated with one or more warranty claims associated with the one or more chillers or chiller components, and parse the warranty claim data to identify the historical operating data by generating the start date associated with the chiller based on at least one of (i) a shipping date associated with a day when the chiller was shipped to a location of operation or (ii) a manufacture date associated with when the chiller was manufactured. In some embodiments, the instructions further cause the one or more processors to parse the historical operating data to identify an element in the historical operating data having at least one of (i) a runtime that is below a threshold runtime, (ii) an event date that is before a threshold event date, or (iii) a failure type that is included in a list of failure types that are below a threshold number of failures, and trim the element from the historical operating data in response. In some embodiments, training the chiller reliability model to produce the trained model includes determining a shape parameter and a scale parameter of a Weibull model. 
     Another implementation of the present disclose is a predictive maintenance system comprising a processing circuit including a processor and memory, the memory having instructions stored thereon that, when executed by the processor, cause the processor to receive historical operating data associated with one or more chillers or chiller components, the historical operating data including two or more event dates associated with the one or more chillers, wherein the two or more event dates include a failure date associated with a failure of the chiller and a start date associated with a day when the chiller came online, calculate a runtime of a chiller of the one or more chillers based on the two or more event dates by determining an amount of time between the failure date and the start date, calibrate the runtime by determining an idle time associated with the chiller corresponding to a location of the chiller and subtracting the idle time from the runtime to generate a calibrated runtime, train a chiller reliability model using the calibrated runtime to produce a shape parameter and a scale parameter of a Weibull model, and generate a reliability metric describing a mean time between failures (MTBF) associated with the chiller using the shape parameter and the scale parameter of the Weibull model. 
     In some embodiments, training the chiller reliability model includes training a Cox model using the calibrated runtime. In some embodiments, the instructions further cause the processor to receive warranty claim data associated with one or more warranty claims associated with the one or more chillers or chiller components, and parse the warranty claim data to identify the historical operating data by generating the start date associated with the chiller based on at least one of (i) a shipping date associated with a day when the chiller was shipped to a location of operation or (ii) a manufacture date associated with when the chiller was manufactured. In some embodiments, the instructions further cause the processor to parse the historical operating data to identify an element in the historical operating data having at least one of (i) a runtime that is below a threshold runtime, (ii) an event date that is before a threshold event date, or (iii) a failure type that is included in a list of failure types that are below a threshold number of failures, and trim the element from the historical operating data in response. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a drawing of a building equipped with a HVAC system, according to some embodiments. 
         FIG.  2    is a schematic diagram of a waterside system which can be used in conjunction with the building of  FIG.  1   , according to some embodiments. 
         FIG.  3    is a schematic diagram of an airside system which can be used in conjunction with the building of  FIG.  1   , according to some embodiments. 
         FIG.  4    is a block diagram of a building management system (BMS) which can be used to monitor and control the building of  FIG.  1   , according to some embodiments. 
         FIG.  5    is a block diagram of another BMS which can be used to monitor and control the building of  FIG.  1   , according to some embodiments. 
         FIG.  6    is a block diagram of a predictive maintenance system for modeling HVAC component reliability, according to some embodiments. 
         FIG.  7    is a block diagram illustrating interactions of the predictive maintenance system of  FIG.  6    with external systems, according to some embodiments. 
         FIGS.  8 A- 8 F  are a flow diagram illustrating data manipulation for generating an HVAC component reliability model, according to some embodiments. 
         FIG.  9 A  is a flow diagram illustrating a method of generating one or more reliability metrics, according to some embodiments. 
         FIG.  9 B  is a flow diagram illustrating a data flow process for generating one or more datasets used to train the HVAC component reliability model of  FIG.  6   , according to some embodiments. 
         FIG.  10    is a table illustrating a number of reliability metrics generated by the predictive maintenance system of  FIG.  6   , according to some embodiments. 
         FIG.  11    is a user interface illustrating a number of reliability metrics, according to some embodiments. 
         FIG.  12    is graph illustrating a reliability metric for a number of HVAC components, according to some embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     Overview 
     Building equipment, such as HVAC systems/components, play a significant role in the functioning of a building. For example, employers may rely on HVAC equipment such as chillers to maintain a comfortable environment for employees during hot summer months. As another example, a restaurant may rely on a chiller to maintain a suitable environment for storing food ingredients and may suffer a significant loss (e.g., due to spoilage, etc.) if the chiller malfunctions. Moreover, in many scenarios HVAC equipment such as chillers significantly contribute to building energy consumption (e.g., make up half of building energy consumption, etc.). Therefore, it may be desirable to properly maintain HVAC equipment such as chillers to ensure optimal functionality and efficient performance (e.g., to prevent performance degradation due to faulty components and/or incorrect operation, etc.). For example, even temporary downtime of a chiller may lead to substantial financial losses (e.g., due to lost employee productivity, spoilage, knock-on component failures, etc.). 
     HVAC equipment such as chillers may be equipped with sensors capable of collecting data regarding the functioning of the HVAC equipment. In various embodiments, the data is used to schedule maintenance to prevent downtime associated with HVAC events such as equipment failures (e.g., due to a failed cooling coil, etc.). Predicting equipment failures prior to their occurrence may save time and money. In various embodiments, machine learning and/or statistical models may be used to predict equipment failures. For example, a machine learning and/or statistical model such as a Weibull model and/or a Cox model may be trained using data from sensors monitoring HVAC equipment and may predict equipment failures associated with the HVAC equipment before they occur. 
     However, the accuracy of machine learning and/or statistical models may rely on the quality of training data used to train the machine learning and/or statistical models. For example, a database of historical component failures may be used to train a machine learning model. To continue the example, if the database includes a large proportion of incorrect data (e.g., false-positive equipment failures, etc.), it may cause the machine learning model to incorrectly predict future equipment failures (e.g., overestimate the probability of future equipment failures, etc.). Therefore, there is a need for systems and methods to intelligently manipulate datasets for training machine learning and/or statistical models to predict equipment failures such as chiller failures. It should be understood that while the present disclosure is described with relation to HVAC chillers, the systems and methods of the present disclosure may be applied to any HVAC equipment/components and is not limited to HVAC chillers. Further, it should be understood that the techniques described herein may be applied to building equipment, building devices, and/or building device components other than HVAC equipment in some implementations. 
     In various embodiments, maintenance data such as a maintenance record extracted from warranty claim data may be used to train a machine learning and/or statistical model. For example, a runtime may be estimated from warranty claim data by comparing a date of a chiller failure to a date the chiller came online. To continue the example, the runtime may be used to train a Weibull model to predict the reliability of chiller components over time. Trained models may generate reliability metrics for individual chiller components, chillers, and/or chiller clusters (and/or other building devices/building device components, etc.). In various embodiments, existing datasets that may be used to train machine learning and/or statistical models may include inherent deficiencies. For example, warranty claim data includes information about chillers that have experienced component failure which may be repaired under the warranty agreement. Since the warranty claim data may only account for chillers that have components that have failed, the warranty claim data may incorrectly skew a machine learning model trained using the warranty claim data to overestimate the likelihood of chiller component failures. To avoid overestimating the likelihood of chiller component failure, warranty claim data and censored chiller data may be combined to be robust against a high false alarm failure rate. For example, taking into account only warranty claim data, the mean time between failures (MTBF) of a chiller may range from 0-5 years. On the other hand, when combining warranty claim data with censored chiller data, the MTBF may range from 25-250 years. Overestimating the likelihood of chiller components may cause unnecessary maintenance leading to an increase in costs for maintaining the chillers. Therefore, systems and methods of the present disclosure may use a combination of warranty claim data and censored chiller data to train the machine learning and/or statistical model to predict the reliability of the chiller components without overestimating the likelihood of chiller component failure. 
     In various embodiments, HVAC equipment/building devices/building device components may follow a “bathtub curve” where equipment/component failures are more common early and late in an equipment/component lifetime. For example, a time-based failure probability for a chiller component may have a first portion associated with a first period of time and a first failure probability, a second portion associated with a second period of time and a second failure probability that is less than the first failure probability, and a third portion associated with a third period of time and a third failure probability that is greater than the second failure probability. In various embodiments, systems and methods of the present disclosure relate to predicting “wear-out” failures associated with the third portion of the time-based failure probability described above. Often, training data for a machine learning and/or statistical model such as warranty claim data may include a number of “early life failures” (e.g., component failures that occur within a threshold time period/number of days of bringing a chiller online such as the first 100 days of operation, etc.) related to the “infant mortality” period (e.g., the first 100 days). However, training a model for predicting wear-out failures using training data that includes infant mortality failures may cause the model to overestimate the likelihood of early-life failures and/or underestimate the lifespan of equipment/components. Therefore, in some embodiments, systems and methods of the present disclosure may trim training data to remove infant mortality data, thereby increasing the accuracy of the model for predicting wear-out failures. 
     In some scenarios, training data may be incomplete. For example, warranty claim data may omit a start date associated with a chiller. In various embodiments, a start date may be used to compute a runtime associated with a chiller. For example, a machine learning model may be trained with runtime data determined by subtracting from a failure date from a start date of a chiller (e.g., the date a chiller became operational for the first time, etc.), thereby determining a time between when a chiller starting functioning (e.g., when it was installed and turned on, etc.) and when it stopped functioning (e.g., due to a failure, etc.). Training a model with incomplete training data may cause the model to be inaccurate (e.g., poorly predict future equipment failures, etc.). Therefore, there is a need for systems and methods to dynamically determine proxy data for incomplete training data. Systems and methods of the present disclosure may update incomplete training data to approximate a missing start date for a chiller using an install date and/or a manufactured date associated with the chiller (and/or other building devices/building device components, etc.). For example, systems and methods of the present disclosure may approximate a start date using an installation date included in warranty claim data. 
     In some scenarios, as described above, runtime data may be used to train a machine learning and/or statistical model to predict equipment/component reliability metrics. In some embodiments, runtime is determined by computing an elapsed time between when a failure occurs and when a piece of equipment came online (e.g., began operating, etc.). Computing the elapsed time may include subtracting a start date from a failure date. However, subtracting a start date from a failure date may overestimate a runtime of equipment/components. For example, a chiller located in Vermont may only be running during a portion of the year (e.g., the summer months, etc.) and may be idle otherwise. To continue the example, subtracting a start date from a failure date may not account for the idle time associated with the chiller, thereby overestimating the amount of time the chiller was actually running (e.g., operating, etc.). Therefore, there is a need for systems and methods to intelligently calibrate runtimes associated with equipment/components to more accurately capture an amount of operating time associated with the equipment/components. Systems and methods of the present disclosure may calibrate equipment/component runtimes using climate data. For example, systems and methods of the present disclosure may determine temperature patterns for an area in which a chiller is installed and use the temperature patterns to update a runtime associated with the chiller to account for a period of time the chiller was idle (e.g., because the temperature was low enough that the chiller wasn&#39;t needed to cool a space, etc.). In various embodiments, calibrating runtime data using climate data may improve an accuracy of a model trained using the runtime data as compared with existing solutions, thereby improving the field of predictive analytics for HVAC equipment/components. 
     In some scenarios, training data may include uncommon equipment/component failures. For example, training data may include data describing a component threading that becomes stripped once in every one-hundred thousand components. In some embodiments, uncommon equipment/component failures may fail to be statistically significant (e.g., have a low occurrence, etc.). Using data that is statistically insignificant to train a model may introduce noise to the model and cause the model to be less accurate. Therefore, there is a need for systems and methods to identify statistically insignificant data in training data. Systems and methods of the present disclosure may analyze training data to trim equipment/component failures that are statistically insignificant (e.g., occur less than a threshold number of times, etc.). 
     Building and HVAC System 
     Referring now to  FIG.  1   , a perspective view of a building  10  is shown. Building  10  is served by a BMS. A BMS is, in general, a system of devices configured to control, monitor, and manage equipment in or around a building or building area. A BMS can include, for example, a HVAC system, a security system, a lighting system, a fire alerting system, any other system that is capable of managing building functions or devices, or any combination thereof. 
     The BMS that serves building  10  includes a HVAC system  100 . HVAC system  100  can include a plurality of HVAC devices (e.g., heaters, chillers, air handling units, pumps, fans, thermal energy storage, etc.) configured to provide heating, cooling, ventilation, or other services for building  10 . For example, HVAC system  100  is shown to include a waterside system  120  and an airside system  130 . Waterside system  120  may provide a heated or chilled fluid to an air handling unit of airside system  130 . Airside system  130  may use the heated or chilled fluid to heat or cool an airflow provided to building  10 . An exemplary waterside system and airside system which can be used in HVAC system  100  are described in greater detail with reference to  FIGS.  2 - 3   . 
     HVAC system  100  is shown to include a chiller  102 , a boiler  104 , and a rooftop air handling unit (AHU)  106 . Waterside system  120  may use boiler  104  and chiller  102  to heat or cool a working fluid (e.g., water, glycol, etc.) and may circulate the working fluid to AHU  106 . In various embodiments, the HVAC devices of waterside system  120  can be located in or around building  10  (as shown in  FIG.  1   ) or at an offsite location such as a central plant (e.g., a chiller plant, a steam plant, a heat plant, etc.). The working fluid can be heated in boiler  104  or cooled in chiller  102 , depending on whether heating or cooling is required in building  10 . Boiler  104  may add heat to the circulated fluid, for example, by burning a combustible material (e.g., natural gas) or using an electric heating element. Chiller  102  may place the circulated fluid in a heat exchange relationship with another fluid (e.g., a refrigerant) in a heat exchanger (e.g., an evaporator) to absorb heat from the circulated fluid. The working fluid from chiller  102  and/or boiler  104  can be transported to AHU  106  via piping  108 . 
     AHU  106  may place the working fluid in a heat exchange relationship with an airflow passing through AHU  106  (e.g., via one or more stages of cooling coils and/or heating coils). The airflow can be, for example, outside air, return air from within building  10 , or a combination of both. AHU  106  may transfer heat between the airflow and the working fluid to provide heating or cooling for the airflow. For example, AHU  106  can include one or more fans or blowers configured to pass the airflow over or through a heat exchanger containing the working fluid. The working fluid may then return to chiller  102  or boiler  104  via piping  110 . 
     Airside system  130  may deliver the airflow supplied by AHU  106  (i.e., the supply airflow) to building  10  via air supply ducts  112  and may provide return air from building  10  to AHU  106  via air return ducts  114 . In some embodiments, airside system  130  includes multiple variable air volume (VAV) units  116 . For example, airside system  130  is shown to include a separate VAV unit  116  on each floor or zone of building  10 . VAV units  116  can include dampers or other flow control elements that can be operated to control an amount of the supply airflow provided to individual zones of building  10 . In other embodiments, airside system  130  delivers the supply airflow into one or more zones of building  10  (e.g., via supply ducts  112 ) without using intermediate VAV units  116  or other flow control elements. AHU  106  can include various sensors (e.g., temperature sensors, pressure sensors, etc.) configured to measure attributes of the supply airflow. AHU  106  may receive input from sensors located within AHU  106  and/or within the building zone and may adjust the flow rate, temperature, or other attributes of the supply airflow through AHU  106  to achieve setpoint conditions for the building zone. 
     Waterside System 
     Referring now to  FIG.  2   , a block diagram of a waterside system  200  is shown, according to some embodiments. In various embodiments, waterside system  200  may supplement or replace waterside system  120  in HVAC system  100  or can be implemented separate from HVAC system  100 . When implemented in HVAC system  100 , waterside system  200  can include a subset of the HVAC devices in HVAC system  100  (e.g., boiler  104 , chiller  102 , pumps, valves, etc.) and may operate to supply a heated or chilled fluid to AHU  106 . The HVAC devices of waterside system  200  can be located within building  10  (e.g., as components of waterside system  120 ) or at an offsite location such as a central plant. 
     In  FIG.  2   , waterside system  200  is shown as a central plant having a plurality of subplants  202 - 212 . Subplants  202 - 212  are shown to include a heater subplant  202 , a heat recovery chiller subplant  204 , a chiller subplant  206 , a cooling tower subplant  208 , a hot thermal energy storage (TES) subplant  210 , and a cold thermal energy storage (TES) subplant  212 . Subplants  202 - 212  consume resources (e.g., water, natural gas, electricity, etc.) from utilities to serve thermal energy loads (e.g., hot water, cold water, heating, cooling, etc.) of a building or campus. For example, heater subplant  202  can be configured to heat water in a hot water loop  214  that circulates the hot water between heater subplant  202  and building  10 . Chiller subplant  206  can be configured to chill water in a cold water loop  216  that circulates the cold water between chiller subplant  206  building  10 . Heat recovery chiller subplant  204  can be configured to transfer heat from cold water loop  216  to hot water loop  214  to provide additional heating for the hot water and additional cooling for the cold water. Condenser water loop  218  may absorb heat from the cold water in chiller subplant  206  and reject the absorbed heat in cooling tower subplant  208  or transfer the absorbed heat to hot water loop  214 . Hot TES subplant  210  and cold TES subplant  212  may store hot and cold thermal energy, respectively, for subsequent use. 
     Hot water loop  214  and cold water loop  216  may deliver the heated and/or chilled water to air handlers located on the rooftop of building  10  (e.g., AHU  106 ) or to individual floors or zones of building  10  (e.g., VAV units  116 ). The air handlers push air past heat exchangers (e.g., heating coils or cooling coils) through which the water flows to provide heating or cooling for the air. The heated or cooled air can be delivered to individual zones of building  10  to serve thermal energy loads of building  10 . The water then returns to subplants  202 - 212  to receive further heating or cooling. 
     Although subplants  202 - 212  are shown and described as heating and cooling water for circulation to a building, it is understood that any other type of working fluid (e.g., glycol, CO2, etc.) can be used in place of or in addition to water to serve thermal energy loads. In other embodiments, subplants  202 - 212  may provide heating and/or cooling directly to the building or campus without requiring an intermediate heat transfer fluid. These and other variations to waterside system  200  are within the teachings of the present disclosure. 
     Each of subplants  202 - 212  can include a variety of equipment configured to facilitate the functions of the subplant. For example, heater subplant  202  is shown to include a plurality of heating elements  220  (e.g., boilers, electric heaters, etc.) configured to add heat to the hot water in hot water loop  214 . Heater subplant  202  is also shown to include several pumps  222  and  224  configured to circulate the hot water in hot water loop  214  and to control the flow rate of the hot water through individual heating elements  220 . Chiller subplant  206  is shown to include a plurality of chillers  232  configured to remove heat from the cold water in cold water loop  216 . Chiller subplant  206  is also shown to include several pumps  234  and  236  configured to circulate the cold water in cold water loop  216  and to control the flow rate of the cold water through individual chillers  232 . 
     Heat recovery chiller subplant  204  is shown to include a plurality of heat recovery heat exchangers  226  (e.g., refrigeration circuits) configured to transfer heat from cold water loop  216  to hot water loop  214 . Heat recovery chiller subplant  204  is also shown to include several pumps  228  and  230  configured to circulate the hot water and/or cold water through heat recovery heat exchangers  226  and to control the flow rate of the water through individual heat recovery heat exchangers  226 . Cooling tower subplant  208  is shown to include a plurality of cooling towers  238  configured to remove heat from the condenser water in condenser water loop  218 . Cooling tower subplant  208  is also shown to include several pumps  240  configured to circulate the condenser water in condenser water loop  218  and to control the flow rate of the condenser water through individual cooling towers  238 . 
     Hot TES subplant  210  is shown to include a hot TES tank  242  configured to store the hot water for later use. Hot TES subplant  210  may also include one or more pumps or valves configured to control the flow rate of the hot water into or out of hot TES tank  242 . Cold TES subplant  212  is shown to include cold TES tanks  244  configured to store the cold water for later use. Cold TES subplant  212  may also include one or more pumps or valves configured to control the flow rate of the cold water into or out of cold TES tanks  244 . 
     In some embodiments, one or more of the pumps in waterside system  200  (e.g., pumps  222 ,  224 ,  228 ,  230 ,  234 ,  236 , and/or  240 ) or pipelines in waterside system  200  include an isolation valve associated therewith. Isolation valves can be integrated with the pumps or positioned upstream or downstream of the pumps to control the fluid flows in waterside system  200 . In various embodiments, waterside system  200  can include more, fewer, or different types of devices and/or subplants based on the particular configuration of waterside system  200  and the types of loads served by waterside system  200 . 
     Airside System 
     Referring now to  FIG.  3   , a block diagram of an airside system  300  is shown, according to some embodiments. In various embodiments, airside system  300  may supplement or replace airside system  130  in HVAC system  100  or can be implemented separate from HVAC system  100 . When implemented in HVAC system  100 , airside system  300  can include a subset of the HVAC devices in HVAC system  100  (e.g., AHU  106 , VAV units  116 , ducts  112 - 114 , fans, dampers, etc.) and can be located in or around building  10 . Airside system  300  may operate to heat or cool an airflow provided to building  10  using a heated or chilled fluid provided by waterside system  200 . 
     In  FIG.  3   , airside system  300  is shown to include an economizer-type air handling unit (AHU)  302 . Economizer-type AHUs vary the amount of outside air and return air used by the air handling unit for heating or cooling. For example, AHU  302  may receive return air  304  from building zone  306  via return air duct  308  and may deliver supply air  310  to building zone  306  via supply air duct  312 . In some embodiments, AHU  302  is a rooftop unit located on the roof of building  10  (e.g., AHU  106  as shown in  FIG.  1   ) or otherwise positioned to receive both return air  304  and outside air  314 . AHU  302  can be configured to operate exhaust air damper  316 , mixing damper  318 , and outside air damper  320  to control an amount of outside air  314  and return air  304  that combine to form supply air  310 . Any return air  304  that does not pass through mixing damper  318  can be exhausted from AHU  302  through exhaust damper  316  as exhaust air  322 . 
     Each of dampers  316 - 320  can be operated by an actuator. For example, exhaust air damper  316  can be operated by actuator  324 , mixing damper  318  can be operated by actuator  326 , and outside air damper  320  can be operated by actuator  328 . Actuators  324 - 328  may communicate with an AHU controller  330  via a communications link  332 . Actuators  324 - 328  may receive control signals from AHU controller  330  and may provide feedback signals to AHU controller  330 . Feedback signals can include, for example, an indication of a current actuator or damper position, an amount of torque or force exerted by the actuator, diagnostic information (e.g., results of diagnostic tests performed by actuators  324 - 328 ), status information, commissioning information, configuration settings, calibration data, and/or other types of information or data that can be collected, stored, or used by actuators  324 - 328 . AHU controller  330  can be an economizer controller configured to use one or more control algorithms (e.g., state-based algorithms, extremum seeking control (ESC) algorithms, proportional-integral (PI) control algorithms, proportional-integral-derivative (PID) control algorithms, model predictive control (MPC) algorithms, feedback control algorithms, etc.) to control actuators  324 - 328 . 
     Still referring to  FIG.  3   , AHU  302  is shown to include a cooling coil  334 , a heating coil  336 , and a fan  338  positioned within supply air duct  312 . Fan  338  can be configured to force supply air  310  through cooling coil  334  and/or heating coil  336  and provide supply air  310  to building zone  306 . AHU controller  330  may communicate with fan  338  via communications link  340  to control a flow rate of supply air  310 . In some embodiments, AHU controller  330  controls an amount of heating or cooling applied to supply air  310  by modulating a speed of fan  338 . 
     Cooling coil  334  may receive a chilled fluid from waterside system  200  (e.g., from cold water loop  216 ) via piping  342  and may return the chilled fluid to waterside system  200  via piping  344 . Valve  346  can be positioned along piping  342  or piping  344  to control a flow rate of the chilled fluid through cooling coil  334 . In some embodiments, cooling coil  334  includes multiple stages of cooling coils that can be independently activated and deactivated (e.g., by AHU controller  330 , by BMS controller  366 , etc.) to modulate an amount of cooling applied to supply air  310 . 
     Heating coil  336  may receive a heated fluid from waterside system  200 (e.g., from hot water loop  214 ) via piping  348  and may return the heated fluid to waterside system  200  via piping  350 . Valve  352  can be positioned along piping  348  or piping  350  to control a flow rate of the heated fluid through heating coil  336 . In some embodiments, heating coil  336  includes multiple stages of heating coils that can be independently activated and deactivated (e.g., by AHU controller  330 , by BMS controller  366 , etc.) to modulate an amount of heating applied to supply air  310 . 
     Each of valves  346  and  352  can be controlled by an actuator. For example, valve  346  can be controlled by actuator  354  and valve  352  can be controlled by actuator  356 . Actuators  354 - 356  may communicate with AHU controller  330  via communications links  358 - 360 . Actuators  354 - 356  may receive control signals from AHU controller  330  and may provide feedback signals to controller  330 . In some embodiments, AHU controller  330  receives a measurement of the supply air temperature from a temperature sensor  362  positioned in supply air duct  312  (e.g., downstream of cooling coil  334  and/or heating coil  336 ). AHU controller  330  may also receive a measurement of the temperature of building zone  306  from a temperature sensor  364  located in building zone  306 . 
     In some embodiments, AHU controller  330  operates valves  346  and  352  via actuators  354 - 356  to modulate an amount of heating or cooling provided to supply air  310  (e.g., to achieve a setpoint temperature for supply air  310  or to maintain the temperature of supply air  310  within a setpoint temperature range). The positions of valves  346  and  352  affect the amount of heating or cooling provided to supply air  310  by cooling coil  334  or heating coil  336  and may correlate with the amount of energy consumed to achieve a desired supply air temperature. AHU  330  may control the temperature of supply air  310  and/or building zone  306  by activating or deactivating coils  334 - 336 , adjusting a speed of fan  338 , or a combination of both. 
     Still referring to  FIG.  3   , airside system  300  is shown to include a building management system (BMS) controller  366  and a client device  368 . BMS controller  366  can include one or more computer systems (e.g., servers, supervisory controllers, subsystem controllers, etc.) that serve as system level controllers, application or data servers, head nodes, or master controllers for airside system  300 , waterside system  200 , HVAC system  100 , and/or other controllable systems that serve building  10 . BMS controller  366  may communicate with multiple downstream building systems or subsystems (e.g., HVAC system  100 , a security system, a lighting system, waterside system  200 , etc.) via a communications link  370  according to like or disparate protocols (e.g., LON, BACnet, etc.). In various embodiments, AHU controller  330  and BMS controller  366  can be separate (as shown in  FIG.  3   ) or integrated. In an integrated implementation, AHU controller  330  can be a software module configured for execution by a processor of BMS controller  366 . 
     In some embodiments, AHU controller  330  receives information from BMS controller  366  (e.g., commands, setpoints, operating boundaries, etc.) and provides information to BMS controller  366  (e.g., temperature measurements, valve or actuator positions, operating statuses, diagnostics, etc.). For example, AHU controller  330  may provide BMS controller  366  with temperature measurements from temperature sensors  362 - 364 , equipment on/off states, equipment operating capacities, and/or any other information that can be used by BMS controller  366  to monitor or control a variable state or condition within building zone  306 . 
     Client device  368  can include one or more human-machine interfaces or client interfaces (e.g., graphical user interfaces, reporting interfaces, text-based computer interfaces, client-facing web services, web servers that provide pages to web clients, etc.) for controlling, viewing, or otherwise interacting with HVAC system  100 , its subsystems, and/or devices. Client device  368  can be a computer workstation, a client terminal, a remote or local interface, or any other type of user interface device. Client device  368  can be a stationary terminal or a mobile device. For example, client device  368  can be a desktop computer, a computer server with a user interface, a laptop computer, a tablet, a smartphone, a PDA, or any other type of mobile or non-mobile device. Client device  368  may communicate with BMS controller  366  and/or AHU controller  330  via communications link  372 . 
     Building Management Systems 
     Referring now to  FIG.  4   , a block diagram of a building management system (BMS)  400  is shown, according to some embodiments. BMS  400  can be implemented in building  10  to automatically monitor and control various building functions. BMS  400  is shown to include BMS controller  366  and a plurality of building subsystems  428 . Building subsystems  428  are shown to include a building electrical subsystem  434 , an information communication technology (ICT) subsystem  436 , a security subsystem  438 , a HVAC subsystem  440 , a lighting subsystem  442 , a lift/escalators subsystem  432 , and a fire safety subsystem  430 . In various embodiments, building subsystems  428  can include fewer, additional, or alternative subsystems. For example, building subsystems  428  may also or alternatively include a refrigeration subsystem, an advertising or signage subsystem, a cooking subsystem, a vending subsystem, a printer or copy service subsystem, or any other type of building subsystem that uses controllable equipment and/or sensors to monitor or control building  10 . In some embodiments, building subsystems  428  include waterside system  200  and/or airside system  300 , as described with reference to  FIGS.  2 - 3   . 
     Each of building subsystems  428  can include any number of devices, controllers, and connections for completing its individual functions and control activities. HVAC subsystem  440  can include many of the same components as HVAC system  100 , as described with reference to  FIGS.  1 - 3   . For example, HVAC subsystem  440  can include a chiller, a boiler, any number of air handling units, economizers, field controllers, supervisory controllers, actuators, temperature sensors, and other devices for controlling the temperature, humidity, airflow, or other variable conditions within building  10 . Lighting subsystem  442  can include any number of light fixtures, ballasts, lighting sensors, dimmers, or other devices configured to controllably adjust the amount of light provided to a building space. Security subsystem  438  can include occupancy sensors, video surveillance cameras, digital video recorders, video processing servers, intrusion detection devices, access control devices and servers, or other security-related devices. 
     Still referring to  FIG.  4   , BMS controller  366  is shown to include a communications interface  407  and a BMS interface  409 . Interface  407  may facilitate communications between BMS controller  366  and external applications (e.g., monitoring and reporting applications  422 , enterprise control applications  426 , remote systems and applications  444 , applications residing on client devices  448 , etc.) for allowing user control, monitoring, and adjustment to BMS controller  366  and/or subsystems  428 . Interface  407  may also facilitate communications between BMS controller  366  and client devices  448 . BMS interface  409  may facilitate communications between BMS controller  366  and building subsystems  428  (e.g., HVAC, lighting security, lifts, power distribution, business, etc.). 
     Interfaces  407 ,  409  can be or include wired or wireless communications interfaces (e.g., jacks, antennas, transmitters, receivers, transceivers, wire terminals, etc.) for conducting data communications with building subsystems  428  or other external systems or devices. In various embodiments, communications via interfaces  407 ,  409  can be direct (e.g., local wired or wireless communications) or via a communications network  446  (e.g., a WAN, the Internet, a cellular network, etc.). For example, interfaces  407 ,  409  can include an Ethernet card and port for sending and receiving data via an Ethernet-based communications link or network. In another example, interfaces  407 ,  409  can include a Wi-Fi transceiver for communicating via a wireless communications network. In another example, one or both of interfaces  407 ,  409  can include cellular or mobile phone communications transceivers. In some embodiments, communications interface  407  is a power line communications interface and BMS interface  409  is an Ethernet interface. In other embodiments, both communications interface  407  and BMS interface  409  are Ethernet interfaces or are the same Ethernet interface. 
     Still referring to  FIG.  4   , BMS controller  366  is shown to include a processing circuit  404  including a processor  406  and memory  408 . Processing circuit  404  can be communicably connected to BMS interface  409  and/or communications interface  407  such that processing circuit  404  and the various components thereof can send and receive data via interfaces  407 ,  409 . Processor  406  can be implemented as a general purpose processor, an application specific integrated circuit (ASIC), one or more field programmable gate arrays (FPGAs), a group of processing components, or other suitable electronic processing components. 
     Memory  408  (e.g., memory, memory unit, storage device, etc.) can include one or more devices (e.g., RAM, ROM, Flash memory, hard disk storage, etc.) for storing data and/or computer code for completing or facilitating the various processes, layers and modules described in the present application. Memory  408  can be or include volatile memory or non-volatile memory. Memory  408  can include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described in the present application. According to some embodiments, memory  408  is communicably connected to processor  406  via processing circuit  404  and includes computer code for executing (e.g., by processing circuit  404  and/or processor  406 ) one or more processes described herein. 
     In some embodiments, BMS controller  366  is implemented within a single computer (e.g., one server, one housing, etc.). In various other embodiments BMS controller  366  can be distributed across multiple servers or computers (e.g., that can exist in distributed locations). Further, while  FIG.  4    shows applications  422  and  426  as existing outside of BMS controller  366 , in some embodiments, applications  422  and  426  can be hosted within BMS controller  366  (e.g., within memory  408 ). 
     Still referring to  FIG.  4   , memory  408  is shown to include an enterprise integration layer  410 , an automated measurement and validation (AM&amp;V) layer  412 , a demand response (DR) layer  414 , a fault detection and diagnostics (FDD) layer  416 , an integrated control layer  418 , and a building subsystem integration later  420 . Layers  410 - 420  can be configured to receive inputs from building subsystems  428  and other data sources, determine optimal control actions for building subsystems  428  based on the inputs, generate control signals based on the optimal control actions, and provide the generated control signals to building subsystems  428 . The following paragraphs describe some of the general functions performed by each of layers  410 - 420  in BMS  400 . 
     Enterprise integration layer  410  can be configured to serve clients or local applications with information and services to support a variety of enterprise-level applications. For example, enterprise control applications  426  can be configured to provide subsystem-spanning control to a graphical user interface (GUI) or to any number of enterprise-level business applications (e.g., accounting systems, user identification systems, etc.). Enterprise control applications  426  may also or alternatively be configured to provide configuration GUIs for configuring BMS controller  366 . In yet other embodiments, enterprise control applications  426  can work with layers  410 - 420  to optimize building performance (e.g., efficiency, energy use, comfort, or safety) based on inputs received at interface  407  and/or BMS interface  409 . 
     Building subsystem integration layer  420  can be configured to manage communications between BMS controller  366  and building subsystems  428 . For example, building subsystem integration layer  420  may receive sensor data and input signals from building subsystems  428  and provide output data and control signals to building subsystems  428 . Building subsystem integration layer  420  may also be configured to manage communications between building subsystems  428 . Building subsystem integration layer  420  translate communications (e.g., sensor data, input signals, output signals, etc.) across a plurality of multi-vendor/multi-protocol systems. 
     Demand response layer  414  can be configured to optimize resource usage (e.g., electricity use, natural gas use, water use, etc.) and/or the monetary cost of such resource usage in response to satisfy the demand of building  10 . The optimization can be based on time-of-use prices, curtailment signals, energy availability, or other data received from utility providers, distributed energy generation systems  424 , from energy storage  427  (e.g., hot TES  242 , cold TES  244 , etc.), or from other sources. Demand response layer  414  may receive inputs from other layers of BMS controller  366  (e.g., building subsystem integration layer  420 , integrated control layer  418 , etc.). The inputs received from other layers can include environmental or sensor inputs such as temperature, carbon dioxide levels, relative humidity levels, air quality sensor outputs, occupancy sensor outputs, room schedules, and the like. The inputs may also include inputs such as electrical use (e.g., expressed in kWh), thermal load measurements, pricing information, projected pricing, smoothed pricing, curtailment signals from utilities, and the like. 
     According to some embodiments, demand response layer  414  includes control logic for responding to the data and signals it receives. These responses can include communicating with the control algorithms in integrated control layer  418 , changing control strategies, changing setpoints, or activating/deactivating building equipment or subsystems in a controlled manner. Demand response layer  414  may also include control logic configured to determine when to utilize stored energy. For example, demand response layer  414  may determine to begin using energy from energy storage  427  just prior to the beginning of a peak use hour. 
     In some embodiments, demand response layer  414  includes a control module configured to actively initiate control actions (e.g., automatically changing setpoints) which minimize energy costs based on one or more inputs representative of or based on demand (e.g., price, a curtailment signal, a demand level, etc.). In some embodiments, demand response layer  414  uses equipment models to determine an optimal set of control actions. The equipment models can include, for example, thermodynamic models describing the inputs, outputs, and/or functions performed by various sets of building equipment. Equipment models may represent collections of building equipment (e.g., subplants, chiller arrays, etc.) or individual devices (e.g., individual chillers, heaters, pumps, etc.). 
     Demand response layer  414  may further include or draw upon one or more demand response policy definitions (e.g., databases, XML files, etc.). The policy definitions can be edited or adjusted by a user (e.g., via a graphical user interface) so that the control actions initiated in response to demand inputs can be tailored for the user&#39;s application, desired comfort level, particular building equipment, or based on other concerns. For example, the demand response policy definitions can specify which equipment can be turned on or off in response to particular demand inputs, how long a system or piece of equipment should be turned off, what setpoints can be changed, what the allowable set point adjustment range is, how long to hold a high demand setpoint before returning to a normally scheduled setpoint, how close to approach capacity limits, which equipment modes to utilize, the energy transfer rates (e.g., the maximum rate, an alarm rate, other rate boundary information, etc.) into and out of energy storage devices (e.g., thermal storage tanks, battery banks, etc.), and when to dispatch on-site generation of energy (e.g., via fuel cells, a motor generator set, etc.). 
     Integrated control layer  418  can be configured to use the data input or output of building subsystem integration layer  420  and/or demand response later  414  to make control decisions. Due to the subsystem integration provided by building subsystem integration layer  420 , integrated control layer  418  can integrate control activities of the subsystems  428  such that the subsystems  428  behave as a single integrated supersystem. In some embodiments, integrated control layer  418  includes control logic that uses inputs and outputs from a plurality of building subsystems to provide greater comfort and energy savings relative to the comfort and energy savings that separate subsystems could provide alone. For example, integrated control layer  418  can be configured to use an input from a first subsystem to make an energy-saving control decision for a second subsystem. Results of these decisions can be communicated back to building subsystem integration layer  420 . 
     Integrated control layer  418  is shown to be logically below demand response layer  414 . Integrated control layer  418  can be configured to enhance the effectiveness of demand response layer  414  by enabling building subsystems  428  and their respective control loops to be controlled in coordination with demand response layer  414 . This configuration may advantageously reduce disruptive demand response behavior relative to conventional systems. For example, integrated control layer  418  can be configured to assure that a demand response-driven upward adjustment to the setpoint for chilled water temperature (or another component that directly or indirectly affects temperature) does not result in an increase in fan energy (or other energy used to cool a space) that would result in greater total building energy use than was saved at the chiller. 
     Integrated control layer  418  can be configured to provide feedback to demand response layer  414  so that demand response layer  414  checks that constraints (e.g., temperature, lighting levels, etc.) are properly maintained even while demanded load shedding is in progress. The constraints may also include setpoint or sensed boundaries relating to safety, equipment operating limits and performance, comfort, fire codes, electrical codes, energy codes, and the like. Integrated control layer  418  is also logically below fault detection and diagnostics layer  416  and automated measurement and validation layer  412 . Integrated control layer  418  can be configured to provide calculated inputs (e.g., aggregations) to these higher levels based on outputs from more than one building subsystem. 
     Automated measurement and validation (AM&amp;V) layer  412  can be configured to verify that control strategies commanded by integrated control layer  418  or demand response layer  414  are working properly (e.g., using data aggregated by AM&amp;V layer  412 , integrated control layer  418 , building subsystem integration layer  420 , FDD layer  416 , or otherwise). The calculations made by AM&amp;V layer  412  can be based on building system energy models and/or equipment models for individual BMS devices or subsystems. For example, AM&amp;V layer  412  may compare a model-predicted output with an actual output from building subsystems  428  to determine an accuracy of the model. 
     Fault detection and diagnostics (FDD) layer  416  can be configured to provide on-going fault detection for building subsystems  428 , building subsystem devices (i.e., building equipment), and control algorithms used by demand response layer  414  and integrated control layer  418 . FDD layer  416  may receive data inputs from integrated control layer  418 , directly from one or more building subsystems or devices, or from another data source. FDD layer  416  may automatically diagnose and respond to detected faults. The responses to detected or diagnosed faults can include providing an alert message to a user, a maintenance scheduling system, or a control algorithm configured to attempt to repair the fault or to work-around the fault. 
     FDD layer  416  can be configured to output a specific identification of the faulty component or cause of the fault (e.g., loose damper linkage) using detailed subsystem inputs available at building subsystem integration layer  420 . In other exemplary embodiments, FDD layer  416  is configured to provide “fault” events to integrated control layer  418  which executes control strategies and policies in response to the received fault events. According to some embodiments, FDD layer  416  (or a policy executed by an integrated control engine or business rules engine) may shut-down systems or direct control activities around faulty devices or systems to reduce energy waste, extend equipment life, or assure proper control response. 
     FDD layer  416  can be configured to store or access a variety of different system data stores (or data points for live data). FDD layer  416  may use some content of the data stores to identify faults at the equipment level (e.g., specific chiller, specific AHU, specific terminal unit, etc.) and other content to identify faults at component or subsystem levels. For example, building subsystems  428  may generate temporal (i.e., time-series) data indicating the performance of BMS  400  and the various components thereof. The data generated by building subsystems  428  can include measured or calculated values that exhibit statistical characteristics and provide information about how the corresponding system or process (e.g., a temperature control process, a flow control process, etc.) is performing in terms of error from its setpoint. These processes can be examined by FDD layer  416  to expose when the system begins to degrade in performance and alert a user to repair the fault before it becomes more severe. 
     Referring now to  FIG.  5   , a block diagram of another building management system (BMS)  500  is shown, according to some embodiments. BMS  500  can be used to monitor and control the devices of HVAC system  100 , waterside system  200 , airside system  300 , building subsystems  428 , as well as other types of BMS devices (e.g., lighting equipment, security equipment, etc.) and/or HVAC equipment. 
     BMS  500  provides a system architecture that facilitates automatic equipment discovery and equipment model distribution. Equipment discovery can occur on multiple levels of BMS  500  across multiple different communications busses (e.g., a system bus  554 , zone buses  556 - 560  and  564 , sensor/actuator bus  566 , etc.) and across multiple different communications protocols. In some embodiments, equipment discovery is accomplished using active node tables, which provide status information for devices connected to each communications bus. For example, each communications bus can be monitored for new devices by monitoring the corresponding active node table for new nodes. When a new device is detected, BMS  500  can begin interacting with the new device (e.g., sending control signals, using data from the device) without user interaction. 
     Some devices in BMS  500  present themselves to the network using equipment models. An equipment model defines equipment object attributes, view definitions, schedules, trends, and the associated BACnet value objects (e.g., analog value, binary value, multistate value, etc.) that are used for integration with other systems. Some devices in BMS  500  store their own equipment models. Other devices in BMS  500  have equipment models stored externally (e.g., within other devices). For example, a zone coordinator  508  can store the equipment model for a bypass damper  528 . In some embodiments, zone coordinator  508  automatically creates the equipment model for bypass damper  528  or other devices on zone bus  558 . Other zone coordinators can also create equipment models for devices connected to their zone busses. The equipment model for a device can be created automatically based on the types of data points exposed by the device on the zone bus, device type, and/or other device attributes. Several examples of automatic equipment discovery and equipment model distribution are discussed in greater detail below. 
     Still referring to  FIG.  5   , BMS  500  is shown to include a system manager  502 ; several zone coordinators  506 ,  508 ,  510  and  518 ; and several zone controllers  524 ,  530 ,  532 ,  536 ,  548 , and  550 . System manager  502  can monitor data points in BMS  500  and report monitored variables to various monitoring and/or control applications. System manager  502  can communicate with client devices  504  (e.g., user devices, desktop computers, laptop computers, mobile devices, etc.) via a data communications link  574  (e.g., BACnet IP, Ethernet, wired or wireless communications, etc.). System manager  502  can provide a user interface to client devices  504  via data communications link  574 . The user interface may allow users to monitor and/or control BMS  500  via client devices  504 . 
     In some embodiments, system manager  502  is connected with zone coordinators  506 - 510  and  518  via a system bus  554 . System manager  502  can be configured to communicate with zone coordinators  506 - 510  and  518  via system bus  554  using a master-slave token passing (MSTP) protocol or any other communications protocol. System bus  554  can also connect system manager  502  with other devices such as a constant volume (CV) rooftop unit (RTU)  512 , an input/output module (IOM)  514 , a thermostat controller  516  (e.g., a TEC5000 series thermostat controller), and a network automation engine (NAE) or third-party controller  520 . RTU  512  can be configured to communicate directly with system manager  502  and can be connected directly to system bus  554 . Other RTUs can communicate with system manager  502  via an intermediate device. For example, a wired input  562  can connect a third-party RTU  542  to thermostat controller  516 , which connects to system bus  554 . 
     System manager  502  can provide a user interface for any device containing an equipment model. Devices such as zone coordinators  506 - 510  and  518  and thermostat controller  516  can provide their equipment models to system manager  502  via system bus  554 . In some embodiments, system manager  502  automatically creates equipment models for connected devices that do not contain an equipment model (e.g., IOM  514 , third party controller  520 , etc.). For example, system manager  502  can create an equipment model for any device that responds to a device tree request. The equipment models created by system manager  502  can be stored within system manager  502 . System manager  502  can then provide a user interface for devices that do not contain their own equipment models using the equipment models created by system manager  502 . In some embodiments, system manager  502  stores a view definition for each type of equipment connected via system bus  554  and uses the stored view definition to generate a user interface for the equipment. 
     Each zone coordinator  506 - 510  and  518  can be connected with one or more of zone controllers  524 ,  530 - 532 ,  536 , and  548 - 550  via zone buses  556 ,  558 ,  560 , and  564 . Zone coordinators  506 - 510  and  518  can communicate with zone controllers  524 ,  530 - 532 ,  536 , and  548 - 550  via zone busses  556 - 560  and  564  using a MSTP protocol or any other communications protocol. Zone busses  556 - 560  and  564  can also connect zone coordinators  506 - 510  and  518  with other types of devices such as variable air volume (VAV) RTUs  522  and  540 , changeover bypass (COBP) RTUs  526  and  552 , bypass dampers  528  and  546 , and PEAK controllers  534  and  544 . 
     Zone coordinators  506 - 510  and  518  can be configured to monitor and command various zoning systems. In some embodiments, each zone coordinator  506 - 510  and  518  monitors and commands a separate zoning system and is connected to the zoning system via a separate zone bus. For example, zone coordinator  506  can be connected to VAV RTU  522  and zone controller  524  via zone bus  556 . Zone coordinator  508  can be connected to COBP RTU  526 , bypass damper  528 , COBP zone controller  530 , and VAV zone controller  532  via zone bus  558 . Zone coordinator  510  can be connected to PEAK controller  534  and VAV zone controller  536  via zone bus  560 . Zone coordinator  518  can be connected to PEAK controller  544 , bypass damper  546 , COBP zone controller  548 , and VAV zone controller  550  via zone bus  564 . 
     A single model of zone coordinator  506 - 510  and  518  can be configured to handle multiple different types of zoning systems (e.g., a VAV zoning system, a COBP zoning system, etc.). Each zoning system can include a RTU, one or more zone controllers, and/or a bypass damper. For example, zone coordinators  506  and  510  are shown as Verasys VAV engines (VVEs) connected to VAV RTUs  522  and  540 , respectively. Zone coordinator  506  is connected directly to VAV RTU  522  via zone bus  556 , whereas zone coordinator  510  is connected to a third-party VAV RTU  540  via a wired input  568  provided to PEAK controller  534 . Zone coordinators  508  and  518  are shown as Verasys COBP engines (VCEs) connected to COBP RTUs  526  and  552 , respectively. Zone coordinator  508  is connected directly to COBP RTU  526  via zone bus  558 , whereas zone coordinator  518  is connected to a third-party COBP RTU  552  via a wired input  570  provided to PEAK controller  544 . 
     Zone controllers  524 ,  530 - 532 ,  536 , and  548 - 550  can communicate with individual BMS devices (e.g., sensors, actuators, etc.) via sensor/actuator (SA) busses. For example, VAV zone controller  536  is shown connected to networked sensors  538  via SA bus  566 . Zone controller  536  can communicate with networked sensors  538  using a MSTP protocol or any other communications protocol. Although only one SA bus  566  is shown in  FIG.  5   , it should be understood that each zone controller  524 ,  530 - 532 ,  536 , and  548 - 550  can be connected to a different SA bus. Each SA bus can connect a zone controller with various sensors (e.g., temperature sensors, humidity sensors, pressure sensors, light sensors, occupancy sensors, etc.), actuators (e.g., damper actuators, valve actuators, etc.) and/or other types of controllable equipment (e.g., chillers, heaters, fans, pumps, etc.). 
     Each zone controller  524 ,  530 - 532 ,  536 , and  548 - 550  can be configured to monitor and control a different building zone. Zone controllers  524 ,  530 - 532 ,  536 , and  548 - 550  can use the inputs and outputs provided via their SA busses to monitor and control various building zones. For example, a zone controller  536  can use a temperature input received from networked sensors  538  via SA bus  566  (e.g., a measured temperature of a building zone) as feedback in a temperature control algorithm. Zone controllers  524 ,  530 - 532 ,  536 , and  548 - 550  can use various types of control algorithms (e.g., state-based algorithms, extremum seeking control (ESC) algorithms, proportional-integral (PI) control algorithms, proportional-integral-derivative (PID) control algorithms, model predictive control (MPC) algorithms, feedback control algorithms, etc.) to control a variable state or condition (e.g., temperature, humidity, airflow, lighting, etc.) in or around building  10 . 
     Referring now to  FIG.  6   , system  600  for generating reliability metrics for building devices/building device components such as HVAC equipment (e.g., chillers, etc.) is shown, according to an exemplary embodiment. In various embodiments, system  600  trains one or more models using training data such as warranty claim data, operational data, and/or manufacturing, shipping, and install data to generate reliability metrics such as mean time between failure (MTBF), failure probability, time to X % failure, and/or the like. System  600  is shown to include predictive maintenance system  602 , knowledge base  620 , chillers  630 , and external systems  640 . In some embodiments, components of system  600  communicate via a network (e.g., such as network  446  described above in relation to  FIG.  4   , etc.). Predictive maintenance system  602  may train a machine learning and/or statistical model such as a Weibull model and/or a Cox model to generate one or more trained models that can be used to generate reliability metrics. Predictive maintenance system  602  may include processing circuit  604 , reliability models  606 , and environmental models  608 . Processing circuit  604  may include processor  610  and memory  612 . Processor  610  may be implemented as a general purpose processor, an application specific integrated circuit (ASIC), one or more field programmable gate arrays (FPGAs), a group of processing components, or other suitable electronic processing components. 
     Memory  612  (e.g., memory, memory unit, storage device, etc.) may include one or more devices (e.g., RAM, ROM, Flash memory, hard disk storage, etc.) for storing data and/or computer code for completing or facilitating the various processes, layers and modules described in the present application. Memory  612  may be or include volatile memory or non-volatile memory. Memory  612  may include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described in the present application. According to some embodiments, memory  612  is communicably connected to processor  610  via processing circuit  604  and includes computer code for executing (e.g., by processing circuit  604  and/or processor  610 ) one or more operations described herein. Memory  612  may include data preparation circuit  614 , trainer circuit  616 , and reliability analysis circuit  618 . Data preparation circuit  614 , trainer circuit  616 , and reliability analysis circuit  618  may be implemented as software (e.g., computer-executable programming code, etc.), hardware (e.g., a logic circuit, etc.), and/or a combination thereof. 
     Data preparation circuit  614  may retrieve data from one or more sources and prepare the data for training a machine learning and/or statistical model. For example, data preparation circuit  614  may retrieve warranty claim data from knowledge base  620  and may compute and calibrate one or more runtimes based on the warranty claim data for use in training a model. In some embodiments, data preparation circuit  614  retrieves data such as historical operating data from knowledge base  620 . Additionally or alternatively, data preparation circuit  614  may retrieve data such as operational data from chillers  630 . In some embodiments, data preparation circuit  614  may retrieve additional data such as climate data from external systems  640 . 
     In various embodiments, data preparation circuit  614  may compute a runtime associated with equipment/components included in historical operating data. For example, data preparation circuit  614  may implement the function: 
       runtime=failure date−start date
 
     where start date corresponds to the date a piece of equipment/component came online (e.g., began to operate, etc.) and failure date corresponds to the date the piece of equipment/component experienced a failure (e.g., a component failure such as a broken cooling valve, etc.). In some embodiments, a plurality of runtimes may be determined for a chiller based on a plurality of failures within the chiller. For example, for a first chiller component failure, a first runtime equals a first failure date minus the start date as described above. For a second chiller component failure, a second runtime equals the first failure date minus a second failure date. Thus the function for calculating a runtime for chiller component after the first failure may be expressed as: 
       runtime n =failure date n −failure date n−1  
 
     In various embodiments herein the runtime based on first failure is used, but it should be understood that runtimes for subsequent failures, or runtimes associated with multiple failures, may be utilized, and all such modifications are contemplated within the scope of the present disclosure. In various embodiments, data preparation circuit  614  may calibrate a runtime using climate data. For example, data preparation circuit  614  may retrieve climate data associated with a location a chiller is installed in and may update a runtime associated with the chiller based on the number of days the location was below a threshold temperature during the operating period of the chiller. It will be appreciated by those skilled in the art that the exact method for computing a runtime associated with equipment/components may vary depending on the type of equipment/components. In various embodiments, data preparation circuit  614  implements the function: 
       runtime=failure date−state date−idle day(s)
 
     where idle day(s) corresponds to a number of days a piece of equipment/component was idle. In various embodiments, data preparation circuit  614  may determine idle day(s) based on climate data. For example, data preparation circuit  614  may perform a lookup using a table listing appropriate idle day(s) values by region (e.g., as stored in environmental models  608 , etc.). Additionally or alternatively, data preparation circuit  614  may determine idle day(s) using operational data from one or more chillers. For example, data preparation circuit  614  may query a chiller to determine an amount of operating time (e.g., hours, days, etc.) associated with the chiller and may compute runtime and/or idle day(s) based on the operating time. 
     In some embodiments, data preparation circuit  614  removes infant mortality data from training data. For example, data preparation circuit  614  may remove entries in retrieved training data corresponding to chillers that have a runtime that is below a threshold (e.g., 100 days, etc.). In some embodiments, data preparation circuit  614  compares a runtime associated with a data entry to a threshold. Additionally or alternatively, data preparation circuit  614  may remove entries from training data corresponding to “stale” data (e.g., data recorded a long time ago, etc.). For example, data preparation circuit  614  may remove entries in retrieved training data corresponding to failures that occurred before 2010. In some embodiments, data preparation circuit  614  compares a date associated with an entry in the training data to a threshold to determine whether the entry should be trimmed. For example, data preparation circuit  614  may trim data that is older than 10 years to prevent data from outdated chiller models being used to train a model to generate reliability metrics for a modern chiller. 
     In some embodiments, data preparation circuit  614  merges data from multiple sources. For example, data preparation circuit  614  may retrieve failure dates associated with chillers from warranty claim data and may retrieve installation dates associated with the chillers from a manufacturing, shipping, and install database. As another example, data preparation circuit  614  may retrieve fault dates associated with an access control device (e.g., an electronic door lock, etc.) from a BMS and may retrieve an installation date associated with the access control device from a maintenance log. In various embodiments, merging multiple data sources may improve data quality, thereby improving the accuracy of models trained using the merged data. Additionally or alternatively, data preparation circuit  614  may analyze training data to identify and trim entries relating to failures that are not statistically significant. For example, data preparation circuit  614  may remove entries in retrieved training data corresponding to component failures that occur less than a threshold number of times (e.g., or represent a threshold proportion of the total population, etc.). 
     Trainer circuit  616  may train one or more models using training data prepared by data preparation circuit  614 . For example, trainer circuit  616  may train a parametric model such as a Weibull model and/or a semi-parametric model such as a Cox model. In various embodiments, training a Weibull model may include determining a shape parameter and/or a scale parameter. For example, trainer circuit  616  may determine a Weibull distribution based on training data using the function: 
     
       
         
           
             
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     where R(t) is the reliability function at time t, F(t) is the probability of failure at time t, η is the Weibull scale parameter, and β is the Weibull shape parameter. In various embodiments, 0&lt;β&lt;1 corresponds to the infant mortality period, β=1 corresponds to the normal life period, and β&gt;1 corresponds to the wear-out period. In some embodiments, trainer circuit  616  trains a machine learning model using a reliability metric from a Weibull model to optimize between a component survival probability, a monetary cost associated with a failure, an operational cost associated with a piece of equipment/component (e.g., from a chiller operating at sub-optimal capacity, etc.), and/or resource constraints. In various embodiments, trainer circuit  616  implements recursive learning by updating a model using feedback. 
     Reliability analysis circuit  618  may use one or more models trained by trainer circuit  616  to generate reliability metrics and/or maintenance recommendations. For example, reliability analysis circuit  618  may retrieve a shape and a scale parameter from a trained Weibull model and use the shape and scale parameter to determine a MTBF metric. As another example, reliability analysis circuit  618  may use a reliability measure associated with a point in time to determine an optimal maintenance plan based on the survival probability of a component at the point in time, a cost associated with a failure of the component, an operational cost of the component, and/or any resource constraints that may exist. In some embodiments, reliability analysis circuit  618  implements the function: 
     
       
         
           
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     In some embodiments, reliability analysis circuit  618  implements the function: 
     
       
         
           
             
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     where f(t) is the probability density function (PDF) of failure at time t. Additionally or alternatively, reliability analysis circuit  618  may implement the function: 
     
       
         
           
             
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     where h(t) is the hazard rate function for the instantaneous conditional probability of failure at time t. In some embodiments, reliability analysis circuit  618  determines a MTBF as: 
     
       
         
           
             
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     where z is a complex number. In some embodiments, reliability analysis circuit  618  determines time to X % failure as: 
     
       
         
           
             
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     where X is a percentage failure (e.g., a likelihood of failure, etc.). 
     In various embodiments, reliability models  606  include a database storing one or more trained models generated by trainer circuit  616 . For example, reliability models  606  may include a number of trained machine learning models (e.g., weights associated with nodes of a neural network, etc.) generated by trainer circuit  616 . As another example, reliability models  606  may include a number of shape and scale parameters corresponding to different trained Weibull models. In some embodiments, different models are used for different pieces of equipment/components. For example, reliability models  606  may include a first model for generating reliability metrics associated with a first component (e.g., a cooling coil, etc.) and may include a second model for generating reliability metrics associated with a second component (e.g., a bracket, etc.). In various embodiments, reliability models  606  may include models associated with individual components, pieces of equipment (e.g., a chiller, an access control device, a security camera, a fire suppression device, etc.) and/or a cluster of equipment/components (e.g., all chillers produced from a certain manufacturing location, all chillers produced in a certain year, all building controllers having a specific firmware version, etc.). 
     Environmental models  608  may include a database storing climate data for calibrating runtimes associated with HVAC equipment. For example, environmental models  608  may include a table listing idle calibration offsets associated with various geographic regions to facilitate calibrating a runtime associated with a chiller. In various embodiments, environmental models  608  include historical data. For example, environmental models  608  may include a climate model including daily temperatures for an area over a five-year period. In some embodiments, predictive maintenance system  602  determines climate data based on operational data received from chillers  630 . For example, predictive maintenance system  602  may receive control signals from chillers  630  indicating when chillers  630  are operating and/or contextual data (e.g., at what load chillers  630  are running, what an indoor temperature setpoint is, what the outdoor air temperature is, etc.) and may store the information in environmental models  608  based on the geography of chillers  630 . 
     Knowledge base  620  may be a database storing data associated with HVAC equipment such as chillers. For example, knowledge base  620  may include warranty claim data describing (i) an equipment/component identifier, (ii) a ship date (e.g., a date a piece of equipment/component was shipped to an install location, etc.), (iii) a failure date (e.g., a date a piece of equipment/component failed, etc.), (iv) a runtime associated with the equipment/component (e.g., runtime may be equal to the subtraction of the start date from the failure date), (v) a start date (e.g., a date the piece of equipment/component began operating at the install location, etc.), (vi) a manufacturing location identifier, (vii) a product description, and/or (viii) a location identifier associated with the install location (e.g., an address, etc.). In some embodiments, knowledge base  620  includes service history data (e.g., a record of maintenance performed on a piece of equipment/component, etc.). It should be understood that while knowledge base  620  is described in relation to including warranty claim data, knowledge base  620  may store any data from which a runtime associated with a piece of equipment/component may be calculated and that the present disclosure is not limited to computations based on warranty claim data. For example, knowledge base  620  may include fault data associated with a number of building devices (e.g., lighting controllers, thermostats, access control devices, etc.). In various embodiments, knowledge base  620  is or includes a digital twin database such as a knowledge graph. For example, knowledge base  620  may include a graph data structure having nodes representing building devices and/or building device components and edges connecting the nodes representing relationships between the building devices and/or building device components. 
     Chillers  630  may be one or multiple chillers, e.g., chiller  102  as described with reference to  FIG.  1   . Chiller sensors  632  can be positioned on, within, and/or adjacent to chillers  630 , according to some embodiments. Further, chiller sensors  632  can be configured to collect a variety of data including usage time, efficiency metrics, input and output quantities, as well as other data. According to some embodiments, chiller sensors  632  can be configured to store and/or communicate collected chiller data. In some embodiments, chillers  630  can also be configured to store and/or communicate collected chiller data from chiller sensors  632 . Predictive maintenance system  602  may receive performance data from chillers  630  and generate equipment/component reliability models for the chillers and utilize the models to determine the likelihood of a failure occurring in the future for chillers  630 . Predictive maintenance system  602  may not be limited to performing failure predictions for chillers and can also be configured to perform failure prediction for other types of building equipment (e.g., air handler unit  106  as described with reference to  FIG.  1   , boiler  104  as described with reference to  FIG.  1   , etc.). 
     External systems  640  may communicate with predictive maintenance system  602 . For example, external systems  640  may include client devices (e.g., such as client devices  448 , etc.) used by building maintenance personnel and may receive maintenance recommendations from predictive maintenance system  602 . As another example, external systems  640  may include a weather reporting system which may communicate historical climate data to predictive maintenance system  602  to facilitate calibrating runtime estimates associated with chillers. As yet another example, external systems  640  may include building controllers (e.g., BMS controller  366 , etc.) and/or remote systems such as a work order management system (e.g., remote systems and applications  444 , etc.) that receive reliability metrics and/or work order requests from predictive maintenance system  602  to facilitate automated work order requests and/or part ordering. 
     Referring now to  FIG.  7   , interactions between predictive maintenance system  602  and external systems is shown, according to an exemplary embodiment. In various embodiments, predictive maintenance system  602  receives external data. For example, predictive maintenance system  602  may receive operational data from chillers, maintenance data (e.g., as included in warranty claim data, etc.) from a warranty claim database, installation data from a manufacturing, shipping, and installation database, climate data from climate models, fault data from a BMS, predictive maintenance data from a BMS, and/or the like. 
     In various embodiments, predictive maintenance system  602  trains a machine learning and/or statistical model using the received data to generate a trained model. In some embodiments, the trained model includes a Weibull model. For example, training a Weibull model may include determining a Weibull shape and scale parameter based on historical equipment/component failure data and/or runtimes determined therefrom. Additionally or alternatively, the trained model may include a Cox model. In various embodiments, predictive maintenance system  602  generates reliability metrics based on the trained models. For example, predictive maintenance system  602  may generate a MTBF metric, a time to X % failure metric, a cumulative distribution function (CDF), a reliability function, a probability distribution function (PDF), a hazard rate function (HRF), and/or other statistical measures. 
     In various embodiments, predictive maintenance system  602  transmits data to external systems. For example, predictive maintenance system  602  may transmit reliability metrics generated by the trained models to external systems. The external systems may include a maintenance planning/schedule optimization system, a work order management system, and/or the like. In some embodiments, predictive maintenance system  602  generates one or more graphical user interfaces (GUIs). For example, predictive maintenance system  602  may publish results generated by the trained models to one or more dashboards. In various embodiments, the dashboards may inform warranty contracts, maintenance service and part sales programs, maintenance reminders, maintenance planning and scheduling, asset-based maintenance budgeting, asset depreciation, maintenance workforce and resource planning, and/or supply chain planning for parts, to name a few non-limiting examples. 
     Turning now to  FIGS.  8 A- 8 F , a flow diagram illustrating method  800  for data manipulation for preparing data for training a reliability model is shown, according to an exemplary embodiment. In various embodiments, method  800  is performed by predictive maintenance system  602 . For example, predictive maintenance system  602  may receive historical installation, maintenance, and operation data from external systems and may perform method  800  to prepare the received data for training a machine learning model to generate reliability metrics. 
     At step  802 , predictive maintenance system  602  may retrieve data from which a runtime associated with building devices/building device components (e.g., chillers and/or chiller components, etc.) can be determined. For example, predictive maintenance system  602  may retrieve warranty claim data describing an installation date and failure date associated with a number of chillers/chiller components. As another example, predictive maintenance system  602  may retrieve fault data describing one or more faults associated with a building device (e.g., an access control device, etc.). As shown, the retrieved data includes (i) a product part description, (ii) a ship date associated with when a product was shipped to a customer, (iii) a failure date associated with when a product experienced a failure (e.g., broke, etc.), (iv) a component description, (v) a start date associated with when a product came online (e.g., began to operate at a customer location, etc.), (vi) a manufacture site, (vii) a product identifier, and (viii) a location (e.g., an install location of the product, an address, etc.). In various embodiments, predictive maintenance system  602  may calculate a runtime for the product based on the start date and the failure date. In various embodiments, the retrieved data may include records associated with chillers/chiller components that never experienced a failure. In various embodiments, predictive maintenance system  602  may calculate a runtime for chillers/chiller components that never experienced a failure using a current date (e.g., runtime=current date−install date, etc.). In various embodiments, predictive maintenance system  602  retrieves data for performing anomaly detection from a digital twin database, such as a knowledge graph. For example, predictive maintenance system  602  may retrieve the data from a building equipment object and/or from an object connected to the building equipment object by a relationship edge. Digital twins and knowledge graphs are discussed in greater detail in U.S. patent application Ser. No. 17/134,659, filed on Dec. 28, 2020, the entire disclosure of which is incorporated by reference herein. 
     At step  804 , predictive maintenance system  602  may calibrate one or more runtimes generated based on the received data to produce calibrated data  808 . For example, predictive maintenance system  602  may adjust runtimes using climate data  806 . In various embodiments, step  804  includes querying a lookup table using a location associated with a building device/building device component (e.g., chiller/chiller component, etc.) to identify an idle adjustment to apply to a runtime associated with a building device/building device component (e.g., chiller/chiller component, BMS device, etc.). For example, predictive maintenance system  602  may identify a runtime and a location associated with a chiller/chiller component, may determine an idle offset to apply to the chiller/chiller component based on climate data  806  associated with the location, and may adjust the runtime based on the idle offset to produce a calibrated runtime for the chiller/chiller component. As another example, predictive maintenance system  602  may identify a runtime and a location associated with an access control device, may determine an idle offset to apply to the access control device based on fault data associated with the access control device, and may adjust the runtime based on the idle offset to produce a calibrated runtime for the access control device. 
     Additionally or alternatively, step  804  may include trimming the received data. For example, predictive maintenance system  602  may trim the received data to remove records based on (i) a lifetime threshold, (ii) a date threshold, and/or (iii) a threshold number of failures. The lifetime threshold may correspond to a threshold amount of runtime. For example, predictive maintenance system  602  may remove records associated with chillers/chiller components that have an associated runtime that is less than a threshold number of days (e.g., 100 days, etc.). In some embodiments, the lifetime threshold is determined dynamically. For example, predictive maintenance system  602  may perform a lookup to determine a custom lifetime threshold for each building device/building device component (e.g., chiller/chiller component, etc.). In various embodiments, trimming the received data according to the lifetime threshold facilitates removing infant mortality data, thereby increasing an accuracy of a resulting model trained using the trimmed data. 
     The date threshold may correspond to a threshold date associated with the records. For example, predictive maintenance system  602  may remove records associated with chillers/chiller components installed before the year 2010. In some embodiments, step  804  includes analyzing metadata associated with the received data to determine a date (e.g., a year, etc.) that the data was recorded. In some embodiments, the date threshold is determined dynamically. For example, predictive maintenance system  602  may determine the date threshold based on a data quality review that determines that data recorded during a particular time period (e.g., May 2001 to June 2003, etc.) is unreliable. 
     The threshold number of failures may correspond to a minimum number of chiller/chiller component failures required to be statistically significant. For example, predictive maintenance system  602  may analyze the received data and determine the number of failures associated with a particular component and may compare the number of failures to a threshold to determine whether the failures associated with the particular component are statistically significant. As another example, predictive maintenance system  602  may analyze the received data to determine a rate of a particular type of failure associated with a particular chiller component, may compare the rate to a threshold rate associated with the particular type of failure and the particular chiller component, and may trim the received data based on the comparison. In some embodiments, predictive maintenance system  602  determines the threshold dynamically. For example, predictive maintenance system  602  may determine the threshold based on a sample size (e.g., the total number of components in circulation, the number of components for which there are records available, etc.). 
     At step  810 , predictive maintenance system  602  may train one or more machine learning and/or statistical models using the calibrated and/or trimmed data. In various embodiments, step  810  includes determining a Weibull shape and/or scale parameter using calibrated data  808 . In some embodiments, step  810  includes generating a Weibull distribution. Additionally or alternatively, step  810  may include generating one or more statistical measures associated with a Weibull distribution. For example, step  810  may include generating a mean, median, standard deviation, and variance associated with a Weibull scale parameter. In various embodiments, predictive maintenance system  602  may generate a Weibull distribution for each chiller/chiller component included in the received data. In various embodiments, the result of step  810  is model  812 . 
     At step  814 , predictive maintenance system  602  may generate results based on the trained machine learning and/or statistical models (e.g., model  812 , etc.). For example, predictive maintenance system  602  may generate failure probability distribution  816  for a chiller component. As another example, predictive maintenance system  602  may generate table  818  summarizing a failure probability and a MTBF metric for a number of chiller components at a customer location. Table  818  includes a number of predicted failures associated with equipment/components installed at customer locations. In various embodiments, table  818  includes a listing of runtimes associated with various components. Additionally or alternatively, table  818  may include a failure probability and/or a MTBF associated with the various components. As yet another example, predictive maintenance system  602  may generate GUI  820  including maintenance and replacement recommendations for various pieces of equipment/components. In some embodiments, predictive maintenance system  602  exports/stores results in electronic storage (e.g., a database, etc.). For example, predictive maintenance system  602  may store the results into a digital twin database, such as a knowledge graph (e.g., in a building equipment object, in a relationship edge, etc.). 
     Turning now to  FIG.  9 A , a flow diagram illustrating method  900  for generating one or more reliability metrics is shown, according to an exemplary embodiment. In various embodiments, predictive maintenance system  602  performs method  900 . At step  905 , predictive maintenance system  602  may retrieve data describing runtimes associated with one or more HVAC components. For example, predictive maintenance system  602  may retrieve data including a date a chiller started operation and a date the chiller experienced a failure and stopped operation. As another example, the predictive maintenance system  602  may retrieve censored chiller data and chiller warranty claim data. In some embodiments, step  905  includes retrieving data from a number of sources. For example, predictive maintenance system  602  may retrieve a first dataset (e.g., chiller warranty claim data) from a warranty claims database and may retrieve a second dataset from a maintenance and repair database. In various embodiments, the one or more HVAC components include chillers and/or chiller components (e.g., cooling coils, etc.). In some embodiments, step  905  includes calculating a runtime using the retrieved data. For example, the retrieved data may include information such as a start date and a failure date and predictive maintenance system  602  may calculate a runtime based on the start date and the failure date. At step  910 , predictive maintenance system  602  may combine censored chiller data and chiller warranty data to create a dataset that is robust to against a high false alarm failure rate as described above. 
     At step  915 , predictive maintenance system  602  may calibrate the runtimes according to at least one of climate data or component data to generate calibrated data. For example, predictive maintenance system  602  may reduce a runtime associated with a chiller component using an idle offset associated with a geographic region the chiller component is installed in. In various embodiments, step  915  includes identifying a geographic location identifier associated with a record entry such as a street address, performing a lookup using the geographic location identifier to determine an idle offset associated with the geographic location identifier, and adjusting a runtime associated with the record entry based on the determined idle offset. 
     In various embodiments, predictive maintenance system  602  retrieves climate data and/or component data from external sources. For example, predictive maintenance system  602  may query a climate model to retrieve a temperature profile including timeseries temperature data associated with a geographic region. The component data may include service data, an installation date, a manufacture date, and/or the like. In various embodiments, step  915  includes updating a runtime based on an approximated start date. For example, the retrieved data may omit a start date used to calculate a runtime and step  915  may include approximating a start date using an installation date and/or a manufacture date and calculating a runtime based on the approximated start date. 
     At step  920 , predictive maintenance system  602  may trim the calibrated data based on at least one of a lifetime threshold, a date threshold, and/or a threshold number of failures to generate training data. In various embodiments, step  920 inc 1 udes removing infant mortality data, stale data (e.g., data recorded before a threshold date, etc.), and/or statistically insignificant data. In various embodiments, step  920  is optional. 
     At step  925 , predictive maintenance system  602  may train one or more models using the training data. For example, predictive maintenance system  602  may train a parametric model such as a Weibull model for each component of a chiller. As another example, predictive maintenance system  602  may train a semi-parametric model such as a Cox model for a cluster of chillers manufactured at a particular location during a particular time period. Training the one or more models may include generating a Weibull distribution using the training data. In some embodiments, method  900  may include recursive training (e.g., step  942 , etc.). In some embodiments, an indicator of whether combined data (e.g., censored data plus warranty claim data) or only uncensored data is being used may be provided to the model as in input so that the model may adjust based on the data used. For example, if combined data is being used, a one may be used as an input into the model. If only uncensored data is being used, a zero may be used as an input to the model. 
     At step  930 , predictive maintenance system  602  may generate one or more reliability metrics based on the one or more models. In various embodiments, step  930  includes determining a Weibull shape and/or scale parameter based on a Weibull distribution. Additionally or alternatively, predictive maintenance system  602  may calculate additional reliability descriptions such as a MTBF, time to X % failure, CDF, reliability function, PDF, and/or HRF. 
     At step  935 , predictive maintenance system  602  may transmit a notification based on the one or more reliability metrics. For example, predictive maintenance system  602  may generate and transmit a maintenance recommendation (e.g., a recommendation to replace a particular component based on a high likelihood that the component will fail imminently, etc.). As another example, predictive maintenance system  602  may automatically generate and transmit a work order request. As yet another example, predictive maintenance system  602  may generate and display a GUI including a dashboard illustrating estimated lifetimes associated with various chiller components at a location. 
     Referring now to  FIGS.  10 - 12   , various results generated by predictive maintenance system  602  are shown, according to various embodiments. In various embodiments, predictive maintenance system  602  displays one or more of the interfaces associated with  FIGS.  10 - 12   .  FIG.  10    illustrates table  1000  including a number of reliability metrics such as a time to 10% life (e.g., “B(10) Life”), a reliability percentage at 1 year (e.g., the probability a component will still be fully functional at one year, etc.), and a current reliability (e.g., “Reliability (t)”). In various embodiments, table  1000  includes reliability metrics specific to particular components of particular chiller models. Additionally or alternatively, table  1000  may include aggregate reliability metrics for entire chillers and/or chiller clusters. 
     Turning now to  FIG.  9 B , a flow diagram illustrating a data flow process  950  for generating one or more datasets used to train the model is shown according to an exemplary embodiment. In various embodiments, predictive maintenance system  602  performs data flow process  950 . The data flow process  950  may begin with two datasets: the warranty dataset  955  and the warranty claim dataset  960 . Warranty dataset  955  may contain warranty information for chillers including but not limited to start date of the warranty, end date of the extended warranty, chiller identification information, and chiller location. Warranty claim dataset  960  contains chiller failure information including but not limited to failed chiller identification information, which component of the chiller failed, date of failure, resolution of warranty claim, and any other comments about the failure of the chiller. Warranty dataset  955  and warranty claim dataset  960  may be combined to create censored data  965 . More specifically the chillers identified from the warranty claim dataset  960  may be subtracted from the warranty dataset  955  to determine censored data  965 . Warranty dataset  955  and warranty claim data set  960  may also be used to determine uncensored data  970 . Uncensored data  970  may be defined as chillers that have failed. In some embodiments, the uncensored data may include warranty information and location information for failed chillers. The censored data  965  and the uncensored data  970  may be combined to create the combined data  975  as discussed above. Combined data  975  and the location information of the chillers may be used to determine the idle days estimation for different climate zones data  980  as described in step  915  above. The combined data  975  and idle days estimation for different climate zones  980  may be used to create the preprocessed data with calibrated run hour  985  as described in step  915  above. The preprocessed data with calibrated run hour  985  may then be filtered as described in step  920  above to create the final data  990  that may be used to train the model as described above. 
       FIG.  11    illustrates GUI  1100  including a number of MTBF metrics associated with various chiller components. In various embodiments, predictive maintenance system  602  generates GUI  1100  based on one or more models trained using historical chiller information. For example, predictive maintenance system  602  may generate a Weibull distribution using historical runtimes associated with chillers and may generate GUI  1100  using the Weibull distribution. In various embodiments, GUI  1100  is color-coded based on the chiller component. For example, an actuator component may be colored red while an angle valve component may be colored green. GUI  1100  may include a number of chillers  1110  each having a number of components  1112 . In various embodiments, predictive maintenance system  602  generates MTBF metric  1114  for each of components  1112 . 
       FIG.  12    illustrates graph  1200  including a number of reliability functions associated with various chiller components plotted over time. The reliability functions may describe a likelihood a chiller component is to fail at a particular point in time based on historical failures associated with the chiller components. Graph  1200  is shown to relate to a particular type of chiller (e.g., a water cooled screw chiller). However, it should be understood that similar graphs may be generated for different chiller types, components thereof, and/or chiller clusters. In various embodiments, predictive maintenance system  602  removed data related to chillers that had a runtime of less than 100 days prior to generating graph  1200  as described in detail above. 
     Configuration of Exemplary Embodiments 
     The construction and arrangement of the systems and methods as shown in the various exemplary embodiments are illustrative only. Although only a few embodiments have been described in detail in this disclosure, many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes and proportions of the various elements, values of parameters, mounting arrangements, use of materials, colors, orientations, etc.). For example, the position of elements can be reversed or otherwise varied, and the nature or number of discrete elements or positions can be altered or varied. Accordingly, all such modifications are intended to be included within the scope of the present disclosure. The order or sequence of any process or method steps can be varied or re-sequenced according to alternative embodiments. Other substitutions, modifications, changes, and omissions can be made in the design, operating conditions and arrangement of the exemplary embodiments without departing from the scope of the present disclosure. 
     The present disclosure contemplates methods, systems and program products on any machine-readable media for accomplishing various operations. The embodiments of the present disclosure can be implemented using existing computer processors, or by a special purpose computer processor for an appropriate system, incorporated for this or another purpose, or by a hardwired system. Embodiments within the scope of the present disclosure include program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored thereon. Such machine-readable media can be any available media that can be accessed by a general purpose or special purpose computer or other machine with a processor. By way of example, such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code in the form of machine-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer or other machine with a processor. Combinations of the above are also included within the scope of machine-readable media. Machine-executable instructions include, for example, instructions and data which cause a general-purpose computer, special purpose computer, or special purpose processing machines to perform a certain operation or group of operations. 
     Although the figures show a specific order of method steps, the order of the steps may differ from what is depicted. Also, two or more steps can be performed concurrently or with partial concurrence. Such variation will depend on the software and hardware systems chosen and on designer choice. All such variations are within the scope of the disclosure. Likewise, software implementations could be accomplished with standard programming techniques with rule-based logic and other logic to accomplish the various connection steps, processing steps, comparison steps and decision steps.