Patent Publication Number: US-7222048-B2

Title: Methods and systems for diagnosing machinery

Description:
BACKGROUND OF THE INVENTION 
     This invention relates generally to the monitoring of machinery, and more particularly to methods and systems for mathematically estimating an equipment failure. 
     At least some known machinery monitoring systems, monitor machine drivers, for example, motors and turbines, or machine driven components, such as, pumps, compressors, and fans. Other known monitoring systems monitor process parameters of a process, for example, piping systems, and machine environmental conditions, such as machine vibration, machine temperature, and machine oil condition. Typically, such monitoring systems are supplied by an original equipment manufacture (OEM) that is responsible for only a portion of a facility, for example, a specific piece of equipment, and as such, the OEM only provides monitoring for equipment provided by that OEM. However, industrial facilities such as power plants, refineries, factories, and commercial facilities, such as, hospitals, high-rise buildings, resorts, and amusement parks utilize a considerable plurality of machine drivers and driven equipment dependently interconnected to form various process systems. An architect/engineer integrates such equipment for an owner or operator of the facility. Monitoring systems supplied by different OEMs communicate with external data collection and control systems, such as distributed control systems (DCS) located at sites that are remote from the monitored equipment, for example, control rooms and/or operating areas. 
     Typically, machine monitoring systems are primarily focused on providing operating indications and controls, trending, and/or datalogging capabilities for future reconstruction of abnormal events. However, known monitoring systems do not analyze the data to estimate when a machinery failure may occur. For example, monitoring systems collect electrical data from a motor, however, the operator must interpret the data to determine if and/or when the motor may reach a critical condition, i.e., when the machine may fail. More specifically, during operation, the operator visually analyzes the trended data to determine if the machine is trending towards a dangerous level. The operator then visually approximates when the machine will reach the dangerous level. For example, the operator may hold a ruler to a display of the trend to visualize at what point of time in the future, the machine may fail during operation. 
     Accordingly, estimating a time when a machine may fail, is generally determined by an operator based on the operator&#39;s visual analysis of the trending data. Thus predicting a time when a machine may fail, varies based on each specific operator&#39;s visual interpretation of the trending data. 
     BRIEF DESCRIPTION OF THE INVENTION 
     In one aspect, a method of predicting the remaining operational life of a component is provided. The method includes generating a plurality of component data utilizing a machinery monitoring system including a database having at least one rule set, the component data indicative of at least one operational parameter of the component, trending the component data utilizing the rule set, and extrapolating the trended data to facilitate predicting the remaining operational life of the component. 
     In another aspect, a computer program for predicting the remaining operational life of a component is provided. The computer program is configured to generate a plurality of component data samples utilizing a machinery monitoring system including a database having at least one rule set, the component data samples indicative of at least one operational parameter of the component, trend the component data samples utilizing the rule set, and extrapolate the trended data to facilitate predicting the remaining operational life of the component. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram an exemplary equipment layout of an industrial plant; 
         FIG. 2  is a block diagram of an exemplary rule set that may be used with the continuous integrated machinery monitoring system (CIMMS) shown in  FIG. 1 ; 
         FIG. 3  illustrates an exemplary flow diagram of a life cycle of the rule set shown in  FIG. 2 ; 
         FIG. 4  is an exemplary rule to predict an equipment failure; and 
         FIG. 5  is a graphical illustration of the results obtained using the rule shown in  FIG. 4 . 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       FIG. 1  is a block diagram an exemplary equipment layout of an industrial plant  10 . Industrial plant  10  may include a plurality of pumps, motors, fans, and process monitoring sensors that are coupled in flow communication through interconnecting piping and coupled in signal communication with a control system through one or more remote input/output (I/O) modules and interconnecting cabling and/or wireless communication. In the exemplary embodiment, industrial plant  10  includes a distributed control system (DCS)  20  including a network backbone  22 . Network backbone  22  may be a hardwired data communication path fabricated from twisted pair cable, shielded coaxial cable or fiber optic cable, for example, or may be at least partially wireless. DCS  20  may also include a processor  24  that is communicatively coupled to equipment that is located at industrial plant  10 , or at remote locations, through network backbone  22 . It is to be understood that any number of machines may be communicatively connected to the network backbone  22 . A portion of the machines may be hardwired to network backbone  22 , and another portion of the machines may be wirelessly coupled to backbone  22  via a base station  26  that is communicatively coupled to DCS  20 . Wireless base station  26  may be used to expand the effective communication range of DCS  20 , such as with equipment or sensors located remotely from industrial plant  10  but, still interconnected to one or more systems within industrial plant  10 . 
     DCS  20  may be configured to receive and display operational parameters associated with a plurality of equipment, and to generate automatic control signals and receive manual control inputs for controlling the operation of the equipment of industrial plant  10 . In the exemplary embodiment, DCS  20  may include a software code segment configured to control processor  24  to analyze data received at DSC  20  that allows for on-line monitoring and diagnosis of the industrial plant machines. Process parameter data may be collected from each machine, including pumps and motors, associated process sensors, and local environmental sensors, including for example, vibration, seismic, ambient temperature and ambient humidity sensors. The data may be pre-processed by a local diagnostic module or a remote input/output module, or may transmitted to DCS  20  in raw form. 
     Specifically, industrial plant  10  may include a first process system  30  that includes a pump  32  coupled to a motor  34  through a coupling  36 , for example a hydraulic coupling, and interconnecting shafts  38 . The combination of pump  32 , motor  34 , and coupling  36 , although comprising separate components, may operate as a single system, such that conditions affecting the operation of one component of the combination may effect each of the other components of the combination. Accordingly, condition monitoring data collected from one component of the combination that indicates a failure of a portion of the component or an impending failure of the component may be sensed at the other components of the combination to confirm the failure of the component and/or facilitate determining a source or root cause of the failure. 
     Pump  32  may be connected to a piping system  40  through one or more valves  42 . Valve  42  may include an actuator  44 , for example, but, not limited to, an air operator, a motor operator, and a solenoid. Actuator  44  may be communicatively coupled to DCS  20  for remote actuation and position indication. In the exemplary embodiment, piping system  40  may include process parameter sensors, such as a pressure sensor  46 , a flow sensor  48 , a temperature sensor  50 , and a differential pressure (DP) sensor  52 . In an alternative embodiment, piping system  40  may include other sensors, such as turbidity, salinity, pH, specific gravity, and other sensors associated with a particular fluid being carried by piping system  40 . Sensors  46 ,  48 ,  50  and  52  may be communicatively coupled to a field module  54 , for example, a preprocessing module, or remote I/O rack. 
     Motor  34  may include one or more of a plurality of sensors (not shown) that are available to monitor the operating condition of electrodynamic machines. Such sensors may be communicatively coupled to field module  54  through an interconnecting conduit  56 , for example, copper wire or cable, fiber cable, and wireless technology. 
     Field module  54  may communicate with DCS  20  through a network segment  58 . The communications may be through any network protocol and may be representative of preprocessed data and or raw data. The data may be transmitted to processor  24  continuously in a real-time environment or to processor  24  intermittently based on an automatic arrangement or a request for data from processor  24 . DCS  20  includes a real time clock in communication with network backbone  22 , for time stamping process variables for time-based comparisons. 
     Piping system  40  may include other process components, such as a tank  60  that may include one or more of a plurality of sensors available for monitoring process parameters associated with tanks, such as, a tank level sensor  62 . Tank  60  may provide a surge volume for fluid pumped by pump  32  and/or may provide suction pressure for downstream components, such as, skid  64 . Skid  64  may be a pre-engineered and prepackaged subsystem of components that may be supplied by an OEM. Skid  64  may include a first pump  66  and a second pump  68 . In the exemplary embodiment, first pump is coupled to a motor that is directly coupled to a power source (not shown) through a circuit breaker (not shown) that may be controlled by DCS  20 . Second pump  68  is coupled to a motor  72  that is coupled to the power source through a variable speed drive (VSD)  74  that controls a rotational speed of motor  72  in response to commands from a skid controller  76 . Each of pumps  66  and  68 , and motors  70  and  72 , and VSD  74  may include one or more sensors associated with respective operating parameters of each type of equipment as described above in relation to pump/motor/coupling  32 ,  34 , and  36  combination. Skid controller  76  receives signals from the sensors and may transmit the signals to DCS  20  without preprocessing or after processing the data in accordance with predetermined algorithms residing within skid controller  76 . Skid controller  76  may also process the signals and generate control signals for one or more of pumps  66  and  68 , and motors  70  and  72 , and VSD  74  without transmitting data to DCS  20 . Skid controller may also receive commands from DCS  20  to modify the operation of skid  64  in accordance therewith. 
     A second piping system  80  may include a fan  82  that receives air from an ambient space  84  and directs the air through a valve or damper  86  to a component, such as a furnace  88 . Damper  86  may include position sensors  90  and  92  to detect an open and closed position of damper  86 . Furnace  88  may include a damper  94  that may be operated by actuator  96 , which may be, for example, a motor actuator, a fluid powered piston actuator, or other actuator, which may be controlled remotely by DCS  20  through a signal transmitted through a conduit (not shown). A second fan  98  may take a suction on furnace  88  to remove combustion gases from furnace  88  and direct the combustion gases to a smoke stack or chimney (not shown) for discharge to ambient space  84 . Fan  98  may be driven by a motor  100  through a shaft  102  coupled between fan  98  and motor  100 . A rotational speed of motor  100  may be controlled by a VSD  104  that may be communicatively coupled to DCS  20  though network backbone  22 . Fan  82  may be driven by an engine  106 , such as an internal combustion engine, or a steam, water, wind, or gas turbine, or other driver, through a coupling  108 , which may be hydraulic or other power conversion device. Each of the components may include various sensors and control mechanisms that may be communicatively coupled to DCS  20  through network backbone  22  or may communicate with DCS  20  through a wireless transmitter/receiver  109  to wireless base station  26 . 
     DCS  20  may operate independently to control industrial plant  10 , or may be communicatively coupled to one or more other control systems  110 . Each control system may communicate with each other and DCS  20  through a network segment  112 , or may communicate through a network topology, for example, a star (not shown). 
     In the exemplary embodiment, plant  10  includes a continuous integrated machinery monitoring system (CIMMS)  114  that communicates with DCS  20  and other control systems  110 . CIMMS  114  may also be embodied in a software program segment executing on DCS  20  and/or one or more of the other control systems  110 . Accordingly, CIMMS  114  may operate in a distributed manner, such that a portion of the software program segment executes on several processors concurrently. As such, CIMMS  114  may be fully integrated into the operation of DCS  20  and other control systems  110 . CIMMS  114  analyzes data received by DCS  20  and the other control systems  110  determine a health the machines and/or a process employing the machines using a global view of the industrial plant  10 . CIMMS  114  analyzes combinations of drivers and driven components, and process parameters associated with each combination to correlate machine health findings of one machine to machine health indications from other machines in the combination, and associated process or environmental data. CIMMS  114  uses direct measurements from various sensors available on each machine and derived quantities from all or a portion of all the sensors in industrial plant  10 . CIMMS  114 , using predetermined analysis rules, determines a failure or impending failure of one machine and automatically, in real-time correlates the data used to determine the failure or impending failure with equivalent data derived from the operating parameters of other components in the combination or from process parameters. CIMMS  114  also provides for performing trend analysis on the machine combinations and displaying data and/or trends in a variety of formats so as to afford a user of CIMMS  114  an ability to quickly interpret the health assessment and trend information provided by CIMMS  114 . 
       FIG. 2  is a block diagram of an exemplary rule set  280  that may be used with CIMMS  114  (shown in  FIG. 1 ). Rule set  280  may be a combination of one or more custom rules, and a series of properties that define the behavior and state of the custom rules. The rules and properties may be bundled and stored in a format of an XML string, which may be encrypted based on a 25 character alphanumeric key when stored to a file. Rule set  280  is a modular knowledge cell that includes one or more inputs  282  and one or more outputs  284 . Inputs  282  may be software ports that direct data from specific locations in CIMMS  114  to rule set  280 . For example, an input from a motor current sensor may be transmitted to a hardware input termination in DCS  20 . DCS  20  may sample the signal at that termination to receive the signal thereon. The signal may then be processed and stored at a location in a memory accessible and/or integral to DCS  20 . A first input  286  of rule set  280  may be mapped to the location in memory such that the contents of the location in memory is available to rule set  280  as an input. Similarly, an output  288  may be mapped to another location in the memory accessible to DCS  20  or to another memory such that the location in memory contains the output  288  of rule set  280 . 
     In the exemplary embodiment, rule set  280  includes one or more rules relating to monitoring and diagnosis of specific problems associated with equipment operating in an industrial plant, such as, for example, a power plant, a refinery, and a chemical processing facility. Although rule set  280  is described in terms of being used with an industrial plant, rule set  280  may be appropriately constructed to capture any knowledge and be used for determining solutions in any field. For example, rule set  280  may contain knowledge pertaining to economic behavior, financial activity, weather phenomenon, design processes, and medical conditions. Rule set  280  may then be used to determine solutions to problems in these fields. Rule set  280  includes knowledge from one or many sources, such that the knowledge is transmitted to any system where rule set  280  is applied. Knowledge is captured in the form of rules that relate outputs  284  to inputs  282  such that a specification of inputs  282  and outputs  284  allows rule set  280  to be applied to CIMMS  114 . Rule set  280  may include only rules specific to a specific plant asset and may be directed to only one possible problem associated with that specific plant asset. For example, rule set  280  may include only rules that are applicable to a motor or a motor/pump combination. Rule set  280  may only include rules that determine a health of the motor/pump combination using vibration data. Rule set  280  may also include rules that determine the health of the motor/pump combination using a suite of diagnostic tools that include, in addition to vibration analysis techniques, but may also include, for example, performance calculational tools and/or financial calculational tools for the motor/pump combination. 
     In operation, rule set  280  is created in a software developmental tool that prompts a user for relationships between inputs  282  and outputs  284 . Inputs  282  may receive data representing, for example digital signals, analog signals, waveforms, manually entered and/or configuration parameters, and outputs from other rule sets. Rules within rule set  280  may include logical rules, numerical algorithms, application of waveform and signal processing techniques, expert system and artificial intelligence algorithms, statistical tools, and any other expression that may relate outputs  284  to inputs  282 . Outputs  284  may be mapped to respective locations in the memory that are reserved and configured to receive each output  284 . CIMMS  114  and DCS  20  may then use the locations in memory to accomplish any monitoring and/or control functions CIMMS  114  and DCS  20  may be programmed to perform. The rules of rule set  280  operate independently of CIMMS  114  and DCS  20 , although inputs  282  may be supplied to rule set  280  and outputs  284  may be supplied to rule set  280 , directly or indirectly through intervening devices. 
       FIG. 3  illustrates an exemplary flow diagram  300  of a life cycle of rule set  280  (shown in  FIG. 2 ). During creation of rule set  280 , in a developmental mode  302 , a human expert in the field wherein rule set  280  is created, divulges knowledge of the field particular to a specific asset using the development tool by authoring one or more rules. The rules are created by creating expressions of relationship between outputs  284  and inputs  282  such that no coding of the rules is needed. Operands may be selected from a library of operands, using graphical methods, for example, using drag and drop on a graphical user interface built into the development tool. A graphical representation of an operand may be selected from a library portion of a screen display (not shown) and dragged and dropped into a rule creation portion. Relationships between input  282  and operands are arranged in a logical display fashion and the user is prompted for values, such as, constants, when appropriate based on specific operands and specific ones of inputs  282  that are selected. As many rules that are needed to capture the knowledge of the expert are created. Accordingly, rule set  280  may include a robust set of diagnostic and/or monitoring rules or a relatively less robust set of diagnostic and/or monitoring rules based on a customers requirements and a state of the art in the particular field of rule set  280 . The development tool provides resources for testing rule set  280  during a test mode  304  of the development to ensure various combinations and values of inputs  282  produce expected outputs at outputs  284 . To protect the knowledge or intellectual property captured in rule set  280 , a developmental encryption code may be used to lock rule set  280  from being altered except by those in possession of the encryption key. For example, the creator of rule set  280  may keep the encryption key to lockout end users of rule set  280 , the creator may sell the encryption key or license it for a period of time, to the end user or third parties, who may then provides services to the end user. 
     After development, rule set  280  may enter a distribution mode wherein rule set  280  is converted to a transmittable form, for example, a XML file that may be transmitted to a customer via e-mail, CD-ROM, link to an Internet site, or any other means for transmission of a computer readable file. Rule set  280  may be encrypted with a distribution encryption code that may prevent the use of rule set  280  unless the end user is authorized by the creator, for example, by purchasing a distribution encryption key. Rule set  280  may be received by an end user through any means by which a computer readable file may be transmitted. A rule set manager  306 , which may be a software platform that forms a portion of CIMMS  114 , may receive the distributable form of rule set  280  and convert it to a format usable by CIMMS  114 . Rule set manger  306  may be a graphical user interface that allows an end user to manipulate one or more rule sets  280  as objects. Rule set manager  306  may be used to apply rule set  280  such that inputs  282  and corresponding locations in memory are mapped correctly and outputs  284  and their corresponding locations in memory are mapped correctly. When initially applied, rule set  280  may be placed into a trial mode  308  wherein rule set  280  operates as created except that notifications of anomalous behavior may be detected by rule set  280  are not distributed or distributed on a limited basis. During trial mode  308 , quality certifications may be performed to ensure rule set  280  operates correctly in an operating environment. When quality certification is complete, rule set  280  may be placed into a commission mode  310  wherein rule set  280  operates on CIMMS  114  with full functionality of the rules within rule set  280 . In another embodiment, rule set  280  includes a life cycle with only two modes, a trial mode and a live mode. In the trial mode, rules run normally except there are no events generated nor notifications sent, and the live mode is substantially similar to commission mode  310 . 
       FIG. 4  is an exemplary rule set  400  that is configured to receive data from a component and analyze the received data to mathematically predict if and/or when the measured data might cross any warning or alarming level in the furniture, thus eliminating any guesswork from the operator. 
     In the exemplary embodiment, rule set  400  includes a plurality of rules that are responsive to rule set  400 . More specifically, and in the exemplary embodiment, rule set  400  includes at least a data collecting rule  410 , a trouble shooting rule  412 , an allowable limits rule  414 , and a data extrapolation rule  416 . In the exemplary embodiment, extrapolation rule  416  receives a plurality of variables that are derived, collected and/or stored within other rules, such as, but not limited to, data collecting rule  410 , troubleshooting rule  412 , and allowable limits rule  414 , for example. 
     During operation, a plurality of data samples generated by data collecting rule  410  are utilized by rule  400  to facilitate predicting if and/or when a component may fail. Although the methods and systems described herein are in relation to extrapolating a failure of an electric motor utilizing a current signal received from the electric motor, it should be realized that rule set  400  is utilized to mathematically predict if and/or when the measured data received from any component might cross any warning or alarming level in the future. Therefore, the electric motor current is utilized herein to describe an exemplary embodiment, and thus not to limit the scope of rule set  400 . 
     Accordingly, rule set  400  includes programming language to organize a first auto-buffer  420  and a second auto-buffer  422 . During operation, first auto-buffer  420  collects and/or receives “Current A” data samples from data collecting rule  410 . Specifically, first auto-buffer  420  samples the electric current n times per minute over m hours. Li the exemplary embodiment, n=1, and m=60. Therefore, and in the exemplary embodiment, the electric motor current data is sampled once per minute for sixty minutes to generate sixty samples per hour of electric motor current data which are then stored in first auto-buffer  420 . The sixty samples of electric motor current data are then averaged together to generate an average motor current data result, i.e. a single data point  424  that is indicative of the average motor current over the proceeding hour of data collection. The average motor data point  424  is then passed to a first manual buffer  430 . In the exemplary embodiment, a single averaged data point  424  from first auto-buffer  420  is communicated to first manual buffer  430  each hour. Moreover, and in the exemplary embodiment, first manual buffer  430  is programmed to store eight data points  424  that are received from first auto-buffer  420 , wherein each data point  424  represents a single hour of electric current data. First manual buffer therefore stores eight numbers representing mean values for “Current A” for eight consecutive hours in the exemplary embodiment. 
     In the exemplary embodiment, second auto-buffer  422  samples the binary data n times per minute over m hours. In the exemplary embodiment, n=1, and m=60, accordingly, and in the exemplary embodiment, the binary data is sampled once per minute for sixty minutes to generate sixty samples of binary data which are stored in second auto-buffer  422 . The sixty samples of binary data are then averaged to together to generate an average binary data result, i.e. all the 0&#39;s and 1&#39;s are added together and divided by the total number of binary data points to generate a single binary data point that is indicative of the average of the binary data over the proceeding hour of data collection. 
     Second auto-buffer  422  collects and/or receives binary data, i.e. 0&#39;s and 1&#39;s from troubleshooting rule  412 . More specifically, troubleshooting rule  412  is configured to monitor the data samples generated by data collecting rule  410 . In the exemplary embodiment, if the data samples generated by data collecting rule  410  are acceptable, troubleshooting rule  412  generates a “1” indication which is communicated to second auto-buffer  422 . Alternatively, if the data samples generated by data collecting rule  410  are not acceptable troubleshooting rule  412  generates a “0” indication, also referred to as a “Not OK Current A” indication, which is communicated with second auto-buffer  422 . Specifically, second auto-buffer  422  is configured to verify that the data samples generated by data collecting rule  410  meet a predetermined threshold, prior to transmitting the samples to first auto-buffer  420 . More specifically, if the sampled data is within a predefined range, troubleshooting rule  412  generates a “1” indication that is transmitted to second auto-buffer  422 . If however, the sampled data is outside the predefined current range, troubleshooting rule  412  generates a “0” indication that is transmitted to second auto-buffer  422 . For example, when the motor is started and/or stopped, the data samples generated by data collecting rule  410  may be greater and/or less than the predefined range, such that troubleshooting rule  412  determines that the data is not reliable. 
     In the exemplary embodiment, Boolean “Not Ok Current A” and Boolean “Diagnostics must be suppressed” are combined together in a rule  418  to produce a Boolean variable with a general notion “Something is not right”, which operates both auto-buffers  420  and  422 , respectively. During operation, if the “Something is not right” variable is true, i.e. the average of the data collected by second auto-buffer  422  is “0”, then first auto-buffer  420  collecting Current A samples is re-set to zero. More specifically, if the variable “Something is not right” is true, i.e. at least one sample (n) in second auto-buffer  422  is zero, thus the average value of all the binary samples within second auto-buffer  422  is less than one, then the sampled data is not communicated to first auto-buffer  420 . For example, if eight consecutive average values, stored in second manual buffer  432 , are summed together, the result will not give the number eight as a sum if the “Something is not right” variable is true for at least one sample. Therefore, the prediction will be suppressed as based upon unreliable data. 
     Alternatively, if eight consecutive average values  424 , stored in second manual buffer  432 , are summed together, and the result is one, i.e. the “Something is not right” variable is false for all samples, then the results from first manual buffer  430  are communicated to extrapolation rule  416  to mathematically and/or visually predict if and/or when the measured data, i.e. electric current, might cross any warning or alarming level in the future. 
     In the exemplary embodiment, the data collected in first manual buffer  430  is utilized by extrapolation rule  416  to generate a graphical illustration of the electric motor current that is based on previous data collected from the electric motor, and to calculate when the electric motor current might cross any warning or alarming level in the future. 
       FIG. 5  is a graphical illustration of a curve generated by extrapolation rule  416 . In the exemplary embodiment, data points  424  received from first manual buffer  430  are plotted on a graph. More specifically, the eight data points  424  communicated from first manual buffer  430  are utilized by extrapolation rule  416  to generate a curve  460 . In the exemplary embodiment, data points  424  are utilized to generate a polynomial curve, such as a straight line “best fit” curve, for example as shown in  FIG. 5 . In another embodiment, data points  424  are utilized to generate an exponential curve. In another embodiment, data points  424  are utilized to generate a Weibull curve. 
     After extrapolation rule  416  has generated curve  460 , extrapolation rule  416  also generates a “time”  472  when curve  460  will cross a predetermined threshold  470  that represents a relatively dangerous operational level, i.e. when the component is likely to experience a failure. In one embodiment, time  472  is represented as a quantity of hours until the motor will reach a predicted failure. In another embodiment, time  472  is represented as a date on which the motor will reach the predicted failure. More specifically, and in the exemplary embodiment, predetermined threshold  470  is communicated from allowable limits rule  414  to extrapolating rule  416 . In the exemplary embodiment, predetermined threshold  470  is the “MaxCurrent” variable stored in allowable limits rule  414 , i.e., the maximum current at which the motor may operate without experiencing a failure. Accordingly, in the exemplary embodiment, extrapolation rule  416  predicts when the motor current will meet and/or exceed the maximum motor current allowable, MaxCurrent, and generates a result, viewable by an operator that indicates this time  472 . 
     In the exemplary embodiment, time  472  is mathematically determined in accordance with the following equations: 
     
       
         
           
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                 ] 
               
             
           
         
       
     
     where y i  . . . y 8  represent values obtained from first manual buffer  430 , i.e. data points  424  that are collected for eight consecutive hours as shown in the “best fit” curve in  FIG. 5 . 
     In the exemplary embodiment, extrapolation rule  416  also generates a reliability factor  474 . In one embodiment, if reliability factor  474  is equal to one, then time  472  is accurate. Alternatively, if reliability factor  474  is equal to zero, then time  472  is not accurate and should be discarded. More specifically, the Boolean operator “Not OK Current A” and the Boolean operator “Something is Not Right” variables are combined in rule  418 . Accordingly, if the output of rule  418  is a “1” then extrapolating rule  416  provides an operator with an indication that the data is reliable, and therefore the estimate until failure is also reliable. However, if the output of rule  418  is a “0” then extrapolating rule  416  provides an operator with an indication that the data is not reliable, and therefore the estimate until failure is also not reliable. Accordingly, rule  400  also includes a method to verify that the estimated time until failure of the component is verified to ensure that the estimate is accurate. 
     A technical effect of the present invention is to provide a rule based computer program that is configured to predict the remaining operational life of a component. Specifically, data from the equipment being monitored, is not only displayed, but also mathematically analyzed. The analysis makes a prediction regarding when the measured data might cross any warning or alarming level in the future, thus eliminating any guesswork from operator. More specifically, the trending data is mathematically processed for the purpose of extrapolation of the trend to the future. Then, any possibility that extrapolated data crosses some dangerous level, is checked, and the time of checking is reported. Further, the rules described herein provide a future time when the equipment may fail by trending the data to visually and mathematically determine when the trended data will reach an alarm level. The rules also suppress any data that is either to far in the past or too far away in the future, and suppress any bad samples within the data. 
     While the present invention is described with reference to an industrial plant, numerous other applications are contemplated. It is contemplated that the present invention may be applied to any control system, including facilities, such as commercial facilities, vehicles, for example ships, aircraft, and trains, and office buildings or a campus of buildings, as well as, refineries and midstream liquids facilities, and facilities that produce discrete product outputs, such as, factories. 
     The above-described systems and methods of predicting an equipment failure is cost-effective and highly reliable for monitoring and managing the operation and maintenance of facilities. More specifically, the methods and systems described herein facilitate determining facility machine health. As a result, the methods and systems described herein facilitate reducing plant operating costs in a cost-effective and reliable manner. 
     Exemplary embodiments of monitoring systems and methods are described above in detail. The systems are not limited to the specific embodiments described herein, but rather, components of each system may be utilized independently and separately from other components described herein. Each system component can also be used in combination with other system components. 
     While the invention has been described in terms of various specific embodiments, those skilled in the art will recognize that the invention can be practiced with modification within the spirit and scope of the claims.