Patent Publication Number: US-2021191378-A1

Title: Robust fault detection and diagnosis by constructing an ensemble of detectors

Description:
BACKGROUND 
     A building management system (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 may include a controller, a heating, ventilation, and air conditioning (HVAC) system, a security system, a lighting system, a fire alerting system, and any other system that is capable of managing building functions or devices, or any combination thereof. A BMS may include a variety of devices (e.g., HVAC devices, controllers, chillers, fans, sensors, etc.) configured to facilitate monitoring and controlling the building space. Devices may be configured to communicate with other devices via a network, such as a Building Automation and Control network (BACnet) or a Multi-service Transport Platform (MSTP) Network. 
     A BMS may include a fault detection and diagnosis (FDD) system to generate notifications based on detected anomalies. For example, a FDD system may sound a fire alarm in response to detecting a high level of smoke. As a further example, a FDD system may display a fault to a BMS operator in response to detecting a malfunctioning HVAC component (e.g., a stalled air compressor, etc.). The FDD system may monitor the BMS (e.g., the observed system) and generate fault notifications for display to a BMS operator based on anomalies detected in the BMS. 
     SUMMARY 
     One embodiment of the disclosure relates to a method of generating a fault determination in a building management system (BMS), the method including receiving signal data, generating, using a number of fault detection models, a number of fault indications based on the signal data, generating, using a weighting function, based on the number of fault indications, a fault score, comparing the fault score to a fault value, and determining, based on the comparison, an existence of a fault. 
     In some embodiments, the fault score indicates a level of confidence that the fault exists. In some embodiments, the fault score indicates the probability that a parameter of the building management system (BMS) has a specific value. In some embodiments, generating the fault score further includes generating a fault confidence based on the number of fault indications, wherein the fault confidence indicates a level of confidence that the fault exists. In some embodiments, generating the number of fault indications further includes generating, by each of the number of fault detection models, an individual fault confidence based on the signal data. In some embodiments, generating the fault confidence includes applying a second weighting function to the number of individual fault confidences based on the probability that the parameter of the building management system (BMS) has the specific value. In some embodiments, the signal data is timeseries data. In some embodiments, the method further includes, in response to determining the existence of the fault, sending an indication of the fault to a building management (BMS) operator. In some embodiments, a first subset of one or more of the number of fault detection models is associated with detecting a first type of fault and a second subset of one or more of the number of fault detection models is associated with detecting a second type of fault. In some embodiments, generating the number of fault indications comprises generating, by the first subset of fault detection models, a first fault indication of the number of fault indications using a first subset of the signal data associated with a first component of the BMS and generating, by the second subset of fault detection models, a second fault indication of the number of fault indications using a second subset of the signal data associated with a second component of the BMS. 
     Another embodiment of the disclosure relates to a building management system (BMS), including a processing circuit including a processor and memory, the memory having instructions stored thereon that, when executed by the processor, cause the processing circuit to receive signal data, generate, using a number of fault detection models, a number of fault indications based on the signal data, generate, using a weighting function, based on the number of fault indications, a fault score, compare the fault score to a fault value, and determine, based on the comparison, an existence of a fault. 
     In some embodiments, the fault score indicates a level of confidence that the fault exists. In some embodiments, the fault score indicates the probability that a parameter of the BMS has a specific value. In some embodiments, generating the fault score further includes generating a fault confidence based on the number of fault indications, wherein the fault confidence indicates a level of confidence that the fault exists. In some embodiments, generating the number of fault indications further includes generating, by each of the number of fault detection models, an individual fault confidence based on the signal data. In some embodiments, generating the fault confidence includes applying a second weighting function to the number of individual fault confidences based on the probability that the parameter of the BMS has the specific value. In some embodiments, the signal data is timeseries data. In some embodiments, the processing circuit further configured to, in response to determining the existence of the fault, send an indication of the fault to a BMS operator. In some embodiments, a first subset of the number of fault detection models is associated with detecting a first type of fault and a second subset of the number of fault detection models is associated with detecting a second type of fault. In some embodiments, generating the number of fault indications comprises generating, by the first subset of fault detection models, a first fault indication of the number of fault indications using a first subset of the signal data associated with a first component of the BMS and generating, by the second subset of fault detection models, a second fault indication of the number of fault indications using a second subset of the signal data associated with a second component of the BMS. 
     Another embodiment of the disclosure relates to a non-transitory computer-readable storage medium having instructions stored thereon that, when executed by a processor, cause the processor to receive signal data, generate, using a number of fault detection models, a number of fault indications based on the signal data, wherein a first subset of one or more of the number of fault detection models is associated with detecting a first type of fault and a second subset of one or more of the number of fault detection models is associated with detecting a second type of fault, wherein generating the number of fault indications comprises generating, by the first subset of fault detection models, a first fault indication of the number of fault indications using a first subset of the signal data associated with a first component and generating, by the second subset of fault detection models, a second fault indication of the number of fault indications using a second subset of the signal data associated with a second component, generate, using a weighting function, based on the number of fault indications, a fault score, compare the fault score to a fault value, and determine, based on the comparison, an existence of a fault. 
     In some embodiments, the fault score indicates a level of confidence that the fault exists. In some embodiments, the fault score indicates the probability that a parameter associated with a system including the first and second components has a specific value. In some embodiments, generating the fault score further includes generating a fault confidence based on the number of fault indications, wherein the fault confidence indicates a level of confidence that the fault exists. In some embodiments, generating the number of fault indications further includes generating, by each of the number of fault detection models, an individual fault confidence based on the signal data. In some embodiments, generating the fault confidence includes applying a second weighting function to the number of individual fault confidences based on the probability that the parameter of the system has the specific value. In some embodiments, the signal data is timeseries data. In some embodiments, the method further includes, in response to determining the existence of the fault, sending an indication of the fault to an operator associated with the system. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various objects, aspects, features, and advantages of the disclosure will become more apparent and better understood by referring to the detailed description taken in conjunction with the accompanying drawings, in which like reference characters identify corresponding elements throughout. In the drawings, like reference numbers generally indicate identical, functionally similar, and/or structurally similar elements. 
         FIG. 1  is a drawing of a building equipped with a heating, ventilation, or air conditioning (HVAC) system, according to an exemplary embodiment. 
         FIG. 2  is a drawing of a waterside system that can be used in combination with the HVAC system of  FIG. 1 , according to an exemplary embodiment. 
         FIG. 3  is a drawing of an airside system that can be used in combination with the HVAC system of  FIG. 1 , according to an exemplary embodiment. 
         FIG. 4  is a block diagram of a building management system that can be used to monitor and control the building and HVAC system of  FIG. 1 , according to an exemplary embodiment. 
         FIG. 5  is a block diagram of another building management system that can be used to monitor and control the building and HVAC system of  FIG. 1 , according to an exemplary embodiment. 
         FIG. 6  is a block diagram of a fault detection and diagnosis (FDD) system that can be used to monitor a component and/or system, according to an exemplary embodiment. 
         FIG. 7  is a data flow diagram of the FDD system of  FIG. 6  generating a fault determination, according to an exemplary embodiment. 
         FIG. 8  is a data flow diagram of the FDD system of  FIG. 6  generating a fault confidence, according to an exemplary embodiment. 
         FIG. 9  is a flow diagram of a process of determining a fault using the FDD system of  FIG. 6 , according to an exemplary embodiment. 
         FIG. 10  is a flow diagram of a process of determining a fault confidence using the FDD system of  FIG. 6 , according to an exemplary embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     Overview 
     Disclosed herein is an improved fault detection and diagnosis (FDD) system including an ensemble of FDD models. The FDD system of the present disclosure may greatly increase the accuracy of fault determinations and provide improved robustness to individual parameter selection. In various embodiments, the FDD system of the present disclosure includes a superposition of FDD models and/or parameters. As a non-limiting example, the FDD system may include ten FDD models, each having a number of different parameters, and may develop a consensus from the fault determinations of the ten FDD models. In some embodiments, the FDD system of the present disclosure facilitates generation of fault confidences. The FDD system of the present disclosure may improve the functioning of computing systems and the field of fault detection generally by greatly improving the accuracy of fault determinations, reducing false fault determinations, improving fault integrity, reducing fault noise (e.g., alarm fatigue), and thereby conserving operator and system resources devoted to investigating fallacious faults. 
     Building HVAC Systems and Building Management Systems 
     Referring now to  FIGS. 1-5 , several building management systems (BMS) and HVAC systems in which the systems and methods of the present disclosure can be implemented are shown, according to some embodiments. In brief overview,  FIG. 1  shows a building  10  equipped with a HVAC system  100 .  FIG. 2  is a block diagram of a waterside system  200  which can be used to serve building  10 .  FIG. 3  is a block diagram of an airside system  300  which can be used to serve building  10 .  FIG. 4  is a block diagram of a BMS which can be used to monitor and control building  10 .  FIG. 5  is a block diagram of another BMS which can be used to monitor and control building  10 . 
     Building and HVAC System 
     Referring particularly 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 . 
     In some embodiments, HVAC system  100  uses free cooling to cool the working fluid. For example, HVAC system  100  can include one or more cooling towers or heat exchangers which transfer heat from the working fluid to outside air. Free cooling can be used as an alternative or supplement to mechanical cooling via chiller  102  when the temperature of the outside air is below a threshold temperature. HVAC system  100  can switch between free cooling and mechanical cooling based on the current temperature of the outside air and/or the predicted future temperature of the outside air. 
     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 . 
     In some embodiments, waterside system  200  uses free cooling to cool the water in cold water loop  216 . For example, the water returning from the building in cold water loop  216  can be delivered to cooling tower subplant  208  and through cooling towers  238 . Cooling towers  238  can remove heat from the water in cold water loop  216  (e.g., by transferring the heat to outside air) to provide free cooling for the water in cold water loop  216 . In some embodiments, waterside system  200  switches between free cooling with cooling tower subplant  208  and mechanical cooling with chiller subplant  208  based on the current temperature of the outside air and/or the predicted future temperature of the outside air. 
     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. 
     In some embodiments, AHU controller  330  uses free cooling to cool supply air  310 . AHU controller  330  can switch between free cooling and mechanical cooling by operating outside air damper  320  and cooling coil  334 . For example, AHU controller  330  can deactivate cooling coil  334  and open outside air damper  320  to allow outside air  314  to enter supply air duct  312  in response to a determination that free cooling is economically optimal. AHU controller  330  can determine whether free cooling is economically optimal based on the temperature of outside air  314  and/or the predicted future temperature of outside air  314 . For example, AHU controller  330  can determine whether the temperature of outside air  314  is predicted to be below a threshold temperature for a predetermined amount of time. 
     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 one embodiment, 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 processes 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 (TOM)  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 . 
     Fault Detection and Diagnosis System 
     Systems and methods of the present disclosure relate generally to fault detection and diagnosis (FDD). Specifically, described herein are systems and methods of FDD based on a number of FDD models in combination, e.g., that develop a fault consensus. In one non-limiting example embodiment, the FDD system includes an ensemble of FDD models that produce individual fault determinations that are collectively analyzed to determine a fault. In some embodiments, each of the FDD models is focused on detecting a different class of faults. For example, a first FDD model may focus on detecting faults related to a security system while a second FDD model may focus on detecting faults related to a HVAC system. Additionally or alternatively, each of the FDD models may analyze a different aspect of the observed system. For example, a first FDD model may analyze sensor data while a second FDD model may analyze aggregate data. In various embodiments, the FDD system may determine fault confidences. For example, the FDD system may generate a fault with an associated confidence of 80%. While the FDD system described herein is generally discussed as being implemented in a building management system (BMS), one of skill in the art will understand that the FDD system described herein may be applied to any system. For example, the FDD system described herein may be implemented in a computer network (e.g., a data-center, a cloud computing system, etc.) to determine faults associated with hardware functioning. As a further example, the FDD system described herein may be implemented in a closed-circuit television (CCTV) network to determine faults associated with video feeds. Additionally or alternatively, the FDD system described herein may be implemented on different levels. For example, a first FDD system may be implemented at a system level to receive HVAC control signals and determine faults associated with the functioning of the overall HVAC system, while a second FDD system may be implemented at a device/component level to receive signal data and determine faults associated with the functioning of the specific device/component. As a concrete example, the FDD system described herein may be implemented in a register-transfer level (RTL) based access door to determine faults related to the functioning of the access door. 
     Systems and methods of the present disclosure offer many benefits over existing systems. A FDD system may build a model characterizing an observed system (e.g., a BMS system, a security system, etc.). In various embodiments, the model represents a baseline of the observed system (e.g., a normal state of the system, etc.). The FDD system may compare data relating to the observed system to the model to determine anomalies (e.g., faults, alarms, etc.). For example, a FDD system may compare a sensor signal to a parameter value to determine if a fault exists. As a concrete example, a FDD system may compare a particulate measurement from an air quality sensor to a threshold value to determine if an air quality fault exists. Traditional FDD systems are sensitive to individual parameter values. To continue the previous example, an improperly high threshold value may cause the FDD system to generate a large number of false negatives and similarly an improperly low threshold value may cause the FDD system to generate a large number of false positives. Therefore, systems and methods of the present disclosure significantly increase the accuracy of FDD systems by applying an ensemble of detectors. Specifically, instead of using a single parameter value, the systems and methods of the present disclosure use multiple parameter values and/or multiple models in parallel to significantly improve the robustness of fault determinations and reduce the sensitivity to individual parameter values. Furthermore, in various embodiments, systems and methods of the present disclosure facilitate fault confidence determinations. For example, instead of determining a fault as a binary (e.g., fault/no-fault), systems and methods of the present disclosure may facilitate determining a fault as a continuous value (e.g., 10% confidence in fault, 70% confidence in fault, etc.). 
     The FDD system described herein improves FDD technology and the field of fault detection generally by significantly improving the robustness of fault determinations. Furthermore, the FDD system described herein conserves personnel and computing resources by reducing or eliminating false alarms (e.g., false positive fault indications). Traditional FDD systems may generate a large number of false positives (e.g., causing alarm fatigue, etc.) that mask legitimate fault determinations and reduce FDD utility to a user. Furthermore, the large number of false positives associated with a traditional FDD system may require investigation by a user. Therefore, the FDD system described herein conserves personnel and computing resources by reducing false positives, thereby reducing user investigation. Additionally, the FDD system described herein reduces the number of false positives that mask legitimate fault determinations, thereby surfacing information to users that is otherwise unavailable (e.g., because of alarm fatigue, etc.). Furthermore, the FDD system described herein may determine a fault as a continuous value, further providing additional information to a user that is otherwise unavailable in traditional FDD systems (e.g., FDD systems that determine a fault as a binary). Traditional FDD systems may require huge datasets to develop models to determine accurate individual parameters. For example, a FDD system applied to a HVAC system in a building with a highly inconsistent environment (e.g., a lobby with an erratic temperature based on people coming and going, etc.) may require an immense dataset to generate a model capable of producing parameter values of any utility (e.g., parameter values that do not result in a high number of false positives or false negatives, etc.). However, the FDD system described herein is relatively insensitive to individual parameter values, therefore the FDD system described herein conserves computing resources by reducing or eliminating elaborate model generation and thereby reducing computing resources dedicated to processing large datasets. 
     Referring now to  FIG. 6 , a system  600  including a building management system  604  in communication with a building network  602  is shown, according to an exemplary embodiment. Building network  602  may include BMS  400  (e.g., BMS controller  366 , building subsystems  428 , etc.) and/or any items (e.g., spaces, equipment, objects, points, etc.) of a building that building management system  604  is associated with. Building management system  604  may be configured to provide various reporting capabilities regarding the items and/or facilitate providing commands (e.g., bulk commands, individual commands, etc.) to one or more of the entities (e.g., building equipment, control objects, control points, etc.) connected therewith. 
     Building management system  604  includes a communications interface  606  and a processing circuit  608  having a processor  610  and a memory  612 . Processing circuit  608  can be communicably connected to communications interface  606  such that processing circuit  608  and the various components thereof can send and receive data via communications interface  606  (e.g., to/from building network  602 , etc.). Processor  610  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  612  (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  612  can be or include volatile memory or non-volatile memory. Memory  612  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 an example embodiment, memory  612  is communicably connected to processor  610  via processing circuit  608  and includes computer code for executing (e.g., by processing circuit  608  and/or processor  610 ) one or more processes described herein. In some embodiments, building management system  604  is implemented within a single computer (e.g., one server, one housing, etc.). In various other embodiments, building management system  604  can be distributed across multiple servers or computers (e.g., that can exist in distributed locations). 
     Still referring to  FIG. 6 , memory  612  is shown to include database  616  and FDD circuit  620 . Database  616  is configured to store data collected from a component/system under observation. For example, FDD circuit  620  may be applied to building management system  602  and database  616  may store data collected from buildings and building management system devices. As a further example, FDD circuit  620  may be applied to a CCTV network and database  616  may store data collected from CCTV devices (e.g., cameras, controllers, etc.). In some embodiments, the data is timeseries data. In various embodiments, the data relates to operation of building  10  and/or BMS  400 . For example, database  616  may include sensor data describing a state of building  10 . Database  616  may be a graph database, MySQL, Oracle, Microsoft SQL, Postgre DB2, document store, search engine, key-value store, etc. In some embodiments, database  616  is organized into sections. Each section can represent inputs received from a specific sensor or building management system device. For example, database  616  may include a timeseries including electrical usage data samples of different building management system devices. Database  616  may store the electrical usage data samples in a column with different data entries associated with times the temperature inputs were taken for each row. Each column in database  616  may represent a different sensor within the building management system devices while each row can represent different time stamps representing the time and date the data was received or the time a sensor sensed the data. 
     FDD circuit  620  may include a number of FDD models  622 , weighting circuit  624 , confidence circuit  626 , and threshold circuit  628 . While FDD circuit  620  is described in reference to building management system  604 , it should be understood that FDD circuit  620  may be implemented in any system. For example, FDD circuit  620  may be implemented in a computer network to determine faults related to computer hardware functioning. FDD models  622  may be configured to receive input data and produce a fault determination. In some embodiments, FDD models  622  may be configured to produce a fault confidence. Each of FDD models  622  may include parameters. For example, a first FDD model  622  may include a first parameter associated with a temperature threshold and a second FDD model  622  may include a second parameter associated with a humidity threshold. In various embodiments, the parameters are unique to each FDD model  622 . Additionally or alternatively, FDD models  622  may share one or more parameters. FDD models  622  may be generated based on training data. For example, FDD models  622  may represent a normal state of the observed system (e.g., building  10 , etc.). In various embodiments, each of FDD models  622  are focused on detecting a different class of faults and/or analyzing a different aspect of the observed system. FDD models  622  may receive input data (e.g., sensor signals from building  10 , computer network traffic, etc.) and generate a fault determination (e.g., fault/no-fault, etc.). FDD models  622  may include Bayesian models, deep neural network models (e.g., convolutional neural networks, etc.), spectral analysis systems, principal component analysis systems, AutoEncoder systems, prediction models, One-Class SVM systems, isolated forest trees, and/or any other model. In various embodiments, FDD models  622  are configured to constrain outputs. For example, FDD models  622  may generate outputs in a standardized form (e.g., binary outputs, continuous outputs between [0, 1], etc.). In some embodiments, FDD models  622  may generate outputs in a non-standardized form and FDD circuit  620  may normalize the outputs. For example, a first FDD model  622  may generate a continuous output between [0, 1] and a second FDD model  622  may generate a continuous output between [0, 100]. FDD circuit  620  may normalize the outputs to a standardized binary form. Normalization may include applying a moving window average, statistical analysis, and/or any other normalization technique. 
     Weighting circuit  624  may receive the fault determinations from FDD models  622  and apply various weights to the fault determinations. For example, a first fault determination received from a first FDD model may be assigned a weight of 0.2 while a second fault determination received from a second FDD model may be assigned a weight of 0.8. The weights assigned to each FDD model  622  output (e.g., fault determination, etc.) may be predetermined. For example, the weights may be determined based on machine learning. As a concrete example, FDD circuit  620  may be trained with a training dataset to determine the weight associated with each of FDD models  622 . Additionally or alternatively, the weights may be determined dynamically. For example, the weights may be determined based on a rules engine. As a further example, the weights may be determined based on user input (e.g., a rule-based expert system, etc.). In various embodiments, the weights sum to 1.0. In various embodiments, weighting circuit  624  generates a sum of the weighted fault determinations. The sum of the weighted fault determinations may be a fault score. 
     Confidence circuit  626  may receive the outputs from FDD models  622  and determine a fault confidence. In some embodiments, confidence circuit  626  receives a fault confidence from each of FDD models  622  and determines an aggregate fault confidence. Additionally or alternatively, confidence circuit  626  may receive fault determinations from FDD models  622  and determine a fault confidence based on the received fault determinations. The fault confidence may represent a confidence in the accuracy of the fault determination. For example, a fault confidence of 80% may correspond to an 80% confidence that the determined fault is accurate (e.g., not a false positive or false negative, etc.). Additionally or alternatively, the fault confidence may be associated with the accuracy of the fault determination. For example, a fault confidence of 80% may indicate that 80% of previous faults having similar characteristics were determined to be accurate faults. Determining a fault confidence is described in greater detail below with reference to  FIG. 8 . 
     Threshold circuit  628  may receive the fault determinations from FDD models  622  and analyze the received fault determinations to generate an aggregate fault determination. In some embodiments, the aggregate fault determination is a composite of the individual fault determinations. In various embodiments, threshold circuit  628  receives a fault score from weighting circuit  624 . Threshold circuit  628  may compare the fault score to a threshold to generate a fault determination. In some embodiments, threshold circuit  628  includes a fault confidence when generating a fault confidence. For example, threshold circuit  628  may compare a fault confidence associated with a fault score to determine if the fault score corresponds to a legitimate fault. As a concrete example, threshold circuit  628  may determine that a fault confidence of 80% is greater than a threshold of 75% and therefore an associated fault score represents a legitimate fault. In some embodiments, threshold circuit  628  generates a fault determination based on multiple factors. As a concrete example, threshold circuit  628  may (i) compare a fault score to a fault threshold, (ii) compare a time associated with each of the outputs from FDD models  622 , and (iii) based on (i) and (ii) generate a fault determination. Additionally or alternatively, threshold circuit  628  may include different factors (e.g., thresholds, etc.) based on the class of fault. Thresholds may be predetermined. For example, a threshold may be determined based on machine learning. As a concrete example, threshold circuit  628  may be trained with a training dataset to determine a threshold that produces accurate fault determinations. Additionally or alternatively, thresholds may be determined dynamically. For example, a threshold may be determined based on a rules engine. In some embodiments, threshold circuit  628  includes a number of rules to facilitate generation of fault determinations. As a further example, a threshold may be determined based on user input (e.g., a rule-based expert system, etc.). Generation of fault determinations is discussed in greater detail below with reference to  FIG. 7 . 
     Referring now to  FIG. 7 , a signal flow diagram illustrating a process  700  for generating a fault determination is shown, according to an exemplary embodiment. At step  702 , FDD models  622  may receive input data from database  616 . Additionally or alternatively, FDD models  622  may receive input data from an observed system. For example, FDD models  622  may receive input data from building  10  and/or BMS  400 . The input data may be sensor data. For example, the input data may be timeseries sensor values. In some embodiments, the input data is vectored. For example, input data may take the form x 1 , x 2 , . . . , x t  where each of x i  has d components such that x i ϵ   d  for i=x 1 , x 2 , . . . , x t . In some embodiments, the input data is multi-variate. For example, a vector index may be associated with a time and a vector value may be associated with a measured value. As a concrete example, a two-dimensional vector such that x i ϵ   2  may include room temperature measurements and energy consumption measurements. 
     At step  704 , FDD models  622  may generate individual fault determinations based on the received input data. Process  700  may include any number of FDD models  622 . In various embodiments, the individual fault determinations are binary (e.g., fault/no-fault, etc.). In some embodiments, FDD models  622  are chosen from a candidate set of FDD models. For example, FDD circuit  620  may evaluate m candidate FDD models and select n models as the FDD models  622  based on which of the m candidate FDD models performed the best (e.g., had the highest fault determination accuracy, lowest number of false fault determinations, etc.). In some embodiments, m&gt;&gt;n. In various embodiments, each of the n FDD models  622  includes parameters Φ 1,1 , Φ 1,2 , . . . , Φ 1,α  where α is the number of parameters associated with the first FDD model  622 . As a concrete example, the parameters Φ 1,1 , Φ 1,2 , . . . , Φ 1,α  may include a window size, a threshold value, and/or any other parameters. In some embodiments, different FDD models  622  include a different number of parameters. 
     At step  706 , weighting circuit  624  may apply a weight to each of the individual fault determinations generated by FDD models  622 . In various embodiments, there are n fault determinations, each corresponding to one of FDD models  622 . Weighting circuit  624  may apply weights w 1 , w 2 , . . . , w n  to the n fault determinations. In some embodiments, the weights w 1 , w 2 , . . . , w n  are predetermined. For example, FDD circuit  620  may generate weights w 1 , w 2 , . . . , w n  using a machine learning algorithm. Additionally or alternatively, the weights w 1 , w 2 , . . . , w n  may be determined dynamically. For example, FDD circuit  620  may receive weights w 1 , w 2 , . . . , w n  from an external distributed processing system based on the specific FDD models  622  being used. As a concrete example, weights w 1 , w 2 , . . . , w n  may be determined by testing a number of candidate weights and selecting the weights that perform the best (e.g., have the highest fault determination accuracy, the lowest number of false fault determinations, etc.) over a training data set. In various embodiments, the weights w 1 , w 2 , . . . , w n  are configured such that: 
     
       
         
           
             
               
                 ∑ 
                 
                   i 
                   = 
                   1 
                 
                 n 
               
                
               
                 w 
                 i 
               
             
             = 
             1 
           
         
       
     
     At step  708 , threshold circuit  628  may sum the n weighted fault determinations. For example, threshold circuit  628  may sum the n weighted fault determinations to produce a fault score: 
     
       
         
           
             ϕ 
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   1 
                 
                 n 
               
                
               
                 
                   w 
                   i 
                 
                  
                 
                   f 
                    
                   
                     ( 
                     
                       
                         x 
                         1 
                       
                       , 
                       
                         x 
                         2 
                       
                       , 
                       … 
                        
                       
                           
                       
                       , 
                       
                           
                       
                        
                       
                         
                           x 
                           t 
                         
                         ; 
                         
                           Φ 
                           
                             i 
                             , 
                             α 
                           
                         
                       
                     
                     ) 
                   
                 
               
             
           
         
       
     
     where φ is the fault score and w i f (x 1 , x 2 , . . . , x t ; Φ i,α ) is the weighted fault determination of the i th  FDD model  622 . 
     At step  710 , threshold circuit  628  compares the fault score φ to a threshold to generate a fault determination. For example, threshold circuit  628  may evaluate:
         if(ω≥T)→fault
 
where T is a threshold value. In various embodiments, the fault score φ is a numerical value. In some embodiments, threshold circuit  628  determines a fault exists (e.g., there is a fault, etc.) if the fault score φ exceeds the threshold T. For example, threshold circuit  628  may compare φ=0.4 to T=0.5 to determine a fault does not exist. Additionally or alternatively, threshold circuit  628  may determine a fault exists if the fault score φ is less than the threshold T. It should be understood that mathematically comparable operations may also be used (e.g., comparing ratios, etc.). In various embodiments, FDD circuit  620  sends an indication of the fault based on the determination of threshold circuit  628 . For example, FDD circuit  620  may send a notification to an operator of BMS  400 .
       

     Referring now to  FIG. 8 , a signal flow diagram illustrating a process  800  for generating a fault confidence is shown, according to an exemplary embodiment. At step  802 , FDD models  622  may receive input data from database  616 . In various embodiments, the input data takes the form of x 1 , x 2 , . . . , x t  as described above with reference to  FIG. 7 . In various embodiments, the input x 1 , x 2 , . . . , x t  is the same input described above in reference to  FIG. 7 . Additionally or alternatively, the input x 1 , x 2 , . . . , x t  may be different input. At step  804 , FDD models  622  may generate individual fault determinations based on the received input data. Additionally or alternatively, FDD models  622  may generate individual fault confidences. For example, a first FDD model  622  may generate a first fault confidence associated with a first fault determination and a second FDD model  622  may generate a second fault confidence associated with a second fault determination. In some embodiments, the FDD models  622  are the same FDD models described above in reference to  FIG. 7 . Additionally or alternatively, the FDD models  622  may be different FDD models. For example, process  700  may include a first set of FDD models  622  and process  800  may include a second set of FDD models  622 . As a concrete example, the FDD models  622  of process  800  may be tailored to generating fault confidences while the FDD models  622  of process  700  may be tailored to generating fault determinations. 
     At step  806 , confidence circuit  626  may apply a weight to each of the individual fault determinations generated by FDD models  622 . Additionally or alternatively, confidence circuit  626  may apply a weight to each of the individual fault confidences generated by FDD models  622 . In various embodiments, the weights applied at step  806  are numerically different than the weights referenced in  FIG. 7 . At step  808 , confidence circuit  626  may sum the weighted fault determinations and generate an aggregate fault confidence. For example, confidence circuit  626  may perform: 
     
       
         
           
             confidence 
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   1 
                 
                 n 
               
                
               
                 
                   w 
                   i 
                 
                  
                 
                   f 
                    
                   
                     ( 
                     
                       
                         x 
                         1 
                       
                       , 
                       
                         x 
                         2 
                       
                       , 
                       … 
                        
                       
                           
                       
                       , 
                       
                         
                           x 
                           t 
                         
                         ; 
                         
                           Φ 
                           
                             i 
                             , 
                             α 
                           
                         
                       
                     
                     ) 
                   
                 
               
             
           
         
       
     
     where w i f (x 1 , x 2 , . . . , x t ; Φ i,α ) is the weighted fault determination of the i th  FDD model  622 . Additionally or alternatively, confidence circuit  626  may sum the weighted fault confidences and generate an aggregate fault confidence. For example, confidence circuit  626  may perform: 
     
       
         
           
             confidence 
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   1 
                 
                 n 
               
                
               
                 
                   w 
                   i 
                 
                  
                 
                   h 
                    
                   
                     ( 
                     
                       
                         x 
                         1 
                       
                       , 
                       
                         x 
                         2 
                       
                       , 
                       … 
                        
                       
                           
                       
                       , 
                       
                         
                           x 
                           t 
                         
                         ; 
                         
                           Φ 
                           
                             i 
                             , 
                             α 
                           
                         
                       
                     
                     ) 
                   
                 
               
             
           
         
       
     
     where w i h(x 1 , x 2 , . . . , x t ; Φ i,α ) is the weighted fault confidence of the i th  FDD model  622 . At step  810 , confidence circuit  626  outputs a fault confidence based on the confidence determined in step  808 . In various embodiments, the fault confidence is a percentage. In some embodiments, the fault confidence is an indicator (e.g., a color, a symbol, etc.). In some embodiments, step  810  includes transmitting an indication of the fault confidence. For example, FDD circuit  620  may send a notification including the fault confidence to an operator of BMS  400 . 
     Referring now to  FIG. 9 , a process  900  for generating a fault determination is shown, according to an embodiment. At step  902 , FDD circuit  620  receives signal data. In various embodiments, the signal data is sensor measurements. For example, the signal data may include an AHU valve position. In various embodiments, the signal data is vectored data as described above. At step  904 , FDD circuit  620 , using a number of FDD models  622 , generates a number of fault indications (e.g., fault/no-fault, etc.) based on the received signal data. Additionally or alternatively, FDD models  622  may generate a specific fault property. For example, fault properties may include a time-of-onset of a fault, a cause of a fault, a fault severity, a risk of system shutdown, an estimation of remaining system lifetime, and/or any other system property. As a concrete example, an FDD model  622  may determine that a fault exists and further determine an individual estimate of a time-of-onset of the fault. 
     At step  906 , weighting circuit  624  may generate, using a weighting function, a fault score based on the number of fault indications. For example, weighting circuit  624  may apply weights as described above in reference to  FIG. 7 . Additionally or alternatively, weighting circuit  624  may generate a probability score (e.g., a probability that the specific fault property has a specific value y). As a non-limiting example, weighting circuit  624  may implement the function: 
     
       
         
           
             
               P 
                
               
                 ( 
                 
                   property 
                   = 
                   y 
                 
                 ) 
               
             
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   1 
                 
                 n 
               
                
               
                 
                   w 
                   i 
                 
                  
                 
                   1 
                   
                     yi 
                     = 
                     y 
                   
                 
               
             
           
         
       
     
     where P is the probability that the specific fault property has the specific value y and 1 yi=y  is an indicator function such that 
     
       
         
           
             
               P 
                
               
                 ( 
                 
                   property 
                   = 
                   y 
                 
                 ) 
               
             
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   1 
                 
                 n 
               
                
               
                 
                   w 
                   i 
                 
                  
                 
                   p 
                   i 
                 
                  
                 
                   y 
                   i 
                 
               
             
           
         
       
     
     Additionally or alternatively, weighting circuit  624  may implement the function: 
     
       
         
           
             
               1 
               
                 yi 
                 = 
                 y 
               
             
              
             
               → 
               
                 
                     
                 
                  
                 
                   
                     y 
                     i 
                   
                   = 
                   y 
                 
                  
                 
                     
                 
               
             
              
             
               
                 1 
                  
                 
                     
                 
                  
                 and 
                  
                 
                     
                 
                  
                 
                   1 
                   
                     yi 
                     = 
                     y 
                   
                 
               
                
               
                 → 
                 
                   
                       
                   
                    
                   
                     
                       y 
                       i 
                     
                     ≠ 
                     y 
                   
                    
                   
                       
                   
                 
               
                
               0. 
             
           
         
       
     
     where y i  is the individual estimated value of the specific fault property produced by the i th  FDD model  622  and p 1  is the estimated probability of the specific fault property produced by the i th  FDD model  622 . In some embodiments, the fault score includes P. 
     At step  908 , threshold circuit  628  determines, based on the fault score an existence of a fault. In various embodiments, step  908  includes comparing the fault score to a threshold as described in above in reference to  FIG. 7 . Additionally or alternatively, step  908  may include determining a specific fault property based on the individual estimated values of the specific fault property. For example, threshold circuit  628  may generate a value of the specific fault property based on a consensus of the individual estimates. In some embodiments, threshold circuit  628  generates the value of the specific fault property based on the individual estimate having the highest probability P. In some embodiments, threshold circuit  624  generates the probability that the value of the specific fault property is at least a threshold value. For example, threshold circuit  624  may implement the function: 
     
       
         
           
             
               P 
                
               
                 ( 
                 
                   property 
                   ≥ 
                   y 
                 
                 ) 
               
             
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   1 
                 
                 n 
               
                
               
                 
                   w 
                   i 
                 
                  
                 
                   p 
                   i 
                 
                  
                 
                   1 
                   
                     
                       y 
                       i 
                     
                     ≥ 
                     y 
                   
                 
               
             
           
         
       
     
     At step  910 , FDD circuit  620  sends an indication of the fault based on the determination (e.g., step  908 ). For example, FDD circuit  620  may send a notification describing the fault and the specific fault property. As a concrete example, FDD circuit  620  may send a push notification to an operator of BMS  400  describing a detected fault and a time-of-onset of the fault. 
     Referring now to  FIG. 10 , a process  1000  for generating a fault confidence is shown, according to an embodiment. At step  1002 , FDD circuit  620  receives signal data. In various embodiments, the signal data is sensor measurements. In some embodiments, the signal data is the same as the signal data of process  900 . At step  1004 , FDD circuit  620 , using a number of FDD models  622 , generates a number of fault confidences based on the signal data. In some embodiments, the fault confidences are confidence intervals (e.g., ranges, etc.). At step  1006 , confidence circuit  626  generates a fault confidence based on the number of fault confidences generated by FDD models  622 . In some embodiments, step  1006  includes applying a weighting function as described above in reference to  FIG. 8 . Additionally or alternatively, step  1006  may include summing the individual fault confidences generated by FDD models  622 . For example, confidence circuit  626  may apply a weight to the individual fault confidences and then may sum the weighted individual fault confidences to produce the fault confidence. In some embodiments, the fault confidence is a numeric value (e.g., a fault confidence score, etc.). Additionally or alternatively, the fault confidence may take other representations (e.g., colors, symbols, etc.). At step  1008 , confidence circuit  626  transmits the fault confidence. For example, confidence circuit  626  may transmit a notification to an operator of BMS  400  indicating the existence of a fault and the confidence of the fault determination. 
     A non-limiting example is as follows: FDD circuit  620  may receive data from a HVAC system associated with a monitored room. For example, FDD circuit  620  may receive room temperature measurements and energy consumption measurements in the form of a two-dimensional timeseries vector where each index represents a one-hour time step. The two-dimensional timeseries vector may take the form x i =(t i ,e i ) where t i  is the temperature and e i  is the energy consumption. FDD circuit  620  may be configured to detect a fault if either the temperature measurements or the energy consumption measurements exceed a threshold. The thresholds may be associated with a state of the monitored system. For example, a first set of thresholds may be associated with an occupied state of the monitored room and a second set of thresholds may be associated with an unoccupied state of the monitored room. For example, FDD circuit  620  may be configured to test the following conditions:
         (t i &gt;T 1  OR t i &lt;T 2  OR e i &gt;T 3 ) AND index i corresponds to an occupied state   OR→fault   (t i &gt;T 4  OR t i &lt;T 5  OR e i &gt;T 6 ) AND index i corresponds to an unoccupied state
 
where T 1 -T 6  are threshold values set manually by a domain expert. Additionally or alternatively, T 1 -T 6  may be determined automatically based on statistical methods. For example, T 1  and T 3  may be determined based on the 90% percentile of temperature and energy consumptions in an occupied state as indicated by historical data. Similarly, T 2  may be determined based on the 10% percentile of temperatures in an occupied state as indicated by historical data. Similarly, T 4 , T 5 , and T 6  may be determined based on historical data from an unoccupied state.
       

     Another non-limiting example is as follows: FDD circuit  620  may receive data from a security system associated with a monitored premises. For example, FDD circuit  620  may receive a Gaussian distribution associated with the loitering time of individuals in a building lobby (e.g., how long an individual remained in the building lobby). FDD circuit  620  may further receive a single-dimensional vector x 1 , x 2 , . . . , x t . FDD circuit  620  may generate a model to determine a fault confidence associated with a fault determination. For example, FDD circuit  620  may analyse historical data x′ 1 , x′ 2 , . . . , x′ h  to generate an FDD model  622  where h is the number of historical data elements. FDD circuit  620  may determine the fault confidence associated with an input vector x based on:
         h(x;μ,Σ)=1− (x;μ,Σ)
 
where h(x;μ, Σ) is the fault confidence associated with the input vector x (e.g., the confidence that the new measurement corresponds to a fault in the observed system, etc.), and  (x;μ, Σ) is the normal distribution based on estimated parameters:
       

     
       
         
           
             
                
                
               
                 ( 
                 
                   
                     x 
                     ; 
                     μ 
                   
                   , 
                   Σ 
                 
                 ) 
               
             
             = 
             
               
                 exp 
                  
                 
                   ( 
                   
                     
                       - 
                       
                         1 
                         2 
                       
                     
                      
                     
                       
                         ( 
                         
                           x 
                           - 
                           μ 
                         
                         ) 
                       
                       t 
                     
                      
                     
                       
                         Σ 
                         
                           - 
                           1 
                         
                       
                        
                       
                         ( 
                         
                           x 
                           - 
                           μ 
                         
                         ) 
                       
                     
                   
                   ) 
                 
               
               
                 
                   
                     
                       ( 
                       
                         2 
                          
                         π 
                       
                       ) 
                     
                     d 
                   
                    
                   
                      
                     Σ 
                      
                   
                 
               
             
           
         
       
     
     where μ is the mean of the Gaussian distribution, Σ is the covariance of the Gaussian distribution, and d is the dimension of the input vector x. Accordingly, each FDD model  622  may generate a fault confidence and confidence circuit  626  may analyse the individual fault confidences to generate a fault confidence as described above with reference to  FIG. 8 . As a concrete example, a first prediction based FDD model  622  may generate a model based on the input vector x and/or historical input data. The first prediction based FDD model  622  may generate predictions of expected next normal observations. For example, for a room temperature at a steady state 70° F., the first prediction based FDD model  622  may generate predictions of next temperature measurements around 70° F. The first prediction based FDD model  622  may compare the predictions to the actual observed value to determine a result (e.g., a difference, etc.). Based on the result, the first prediction based FDD model  622  may generate a binary fault output. For example, the first prediction based FDD model  622  may generate a fault indication if the result exceeds a threshold. As a further example, a second deep neural network FDD model  622  may generate a model based on the input vector x and/or historical input data. The second deep neural network FDD model  622  may pass the input vector x through a number of layers of the model and generate a binary fault output. 
     Additionally or alternatively, the input vector x 1 , x 2 , . . . , x t  may be a multivariate timeseries vector. Confidence circuit  626  may then implement the function: 
     
       
         
           
             
               h 
                
               
                 ( 
                 
                   
                     x 
                     1 
                   
                   , 
                   … 
                    
                   
                       
                   
                   , 
                   
                     
                       x 
                       t 
                     
                     ; 
                     µ 
                   
                   , 
                   Σ 
                 
                 ) 
               
             
             = 
             
               
                 max 
                 
                   
                     i 
                     = 
                     1 
                   
                   , 
                   … 
                    
                   
                       
                   
                   , 
                   t 
                 
               
                
               
                   
               
                
               
                 h 
                  
                 
                   ( 
                   
                     
                       
                         x 
                         i 
                       
                       ; 
                       µ 
                     
                     , 
                     Σ 
                   
                   ) 
                 
               
             
           
         
       
     
     where h(x 1 , . . . , x t ;μ,Σ) is the fault confidence associated with the multivariate timeseries input vector x 1 , . . . , x t . In various embodiments, h(x 1 , . . . , x t ;μ,Σ) is associated with the probability of a fault. In various embodiments, the probability of fault is in the interval [0,1]. Similarly as above, each FDD model  622  may generate a fault confidence and confidence circuit  626  may analyze the individual fault confidences to generate a fault confidence as described above with reference to  FIG. 8 . 
     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 function or group of functions. 
     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.