Method and system of monitoring, sensor validation and predictive fault analysis
First Claim
1. A method of monitoring, validation and analysis for a process control system having normal process data and sensors, and represented by a plurality of primary process models and a plurality of process variables, comprising:
- generating a plurality of primary residual process models derived from said primary process models, said normal process data, and one or more statistical parameters corresponding to said normal process data;
measuring real-time sensor data;
computing primary residual values of said primary residual process models corresponding to said real-time sensor data;
deriving an expected value for one or more of said primary residual values from historical normal process data;
determining deviations of said primary residual values from said corresponding expected values;
mapping one or more of said deviations of said primary residual values from said corresponding expected values into one or more compressed values;
automatically generating one or more fuzzy logic diagnostic rules;
computing a certainty factor for a possible fault by applying said fuzzy logic diagnostic rules to said compressed values; and
displaying said certainty factor on a monitor, computer screen or control console used for monitoring, validation and analysis for a process control system.
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Abstract
The present invention provides an improved method and system for real-time monitoring, validation, optimization and predictive fault analysis in a process control system. The invention monitors process operations by continuously analyzing sensor measurements and providing predictive alarms using models of normal process operation and statistical parameters corresponding to normal process data, and generating secondary residual process models. The invention allows for the creation of a fault analyzer directly from linearly independent models of normal process operation, and provides for automatic generation from such process models of linearly dependent process models. Fuzzy logic is used in various fault situations to compute certainty factors to identify faults and/or validate underlying assumptions. In one aspect, the invention includes a real-time sensor data communications bridge module; a state transition logic module; a sensor validation and predictive fault analysis module; and a statistical process control module; wherein each of the modules operates simultaneously.
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Citations
116 Claims
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1. A method of monitoring, validation and analysis for a process control system having normal process data and sensors, and represented by a plurality of primary process models and a plurality of process variables, comprising:
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generating a plurality of primary residual process models derived from said primary process models, said normal process data, and one or more statistical parameters corresponding to said normal process data; measuring real-time sensor data; computing primary residual values of said primary residual process models corresponding to said real-time sensor data; deriving an expected value for one or more of said primary residual values from historical normal process data; determining deviations of said primary residual values from said corresponding expected values; mapping one or more of said deviations of said primary residual values from said corresponding expected values into one or more compressed values; automatically generating one or more fuzzy logic diagnostic rules; computing a certainty factor for a possible fault by applying said fuzzy logic diagnostic rules to said compressed values; and displaying said certainty factor on a monitor, computer screen or control console used for monitoring, validation and analysis for a process control system. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27)
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28. A method of monitoring, validation and analysis for a process control system having normal process data and sensors, and represented by a plurality of primary residual process models and a plurality of process variables, comprising:
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measuring real-time sensor data; computing primary residual values of said primary residual process models corresponding to said real-time sensor data; deriving an expected value for one or more of said primary residual values from historical normal process data; determining deviations of said primary residual values from said corresponding expected values; mapping one or more of said deviations of said primary residual values from said corresponding expected values into one or more compressed values; automatically generating one or more fuzzy logic diagnostic rules; computing a certainty factor for a possible fault by applying said fuzzy logic diagnostic rules to said compressed values; and displaying said certainty factor on a monitor, computer screen or control console used for monitoring, validation and analysis for a process control system. - View Dependent Claims (29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46)
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47. A method of monitoring, validation and analysis for a process control system having normal process data and sensors, and represented by a plurality of primary process models and a plurality of process variables, comprising:
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generating a plurality of primary residual process models derived from said primary process models, said normal process data, and one or more statistical parameters corresponding to said normal process data; automatically generating one or more secondary residual process models, wherein each of said secondary residual process models is derived from two primary residual process models having at least one common variable by combining said primary residual process models to remove any terms containing said common variable; mapping one or more residual values into one or more compressed values; using one or more of said primary residual process models, one or more of said secondary residual process models and said compressed values to predict a possible fault; and displaying said possible fault on a monitor, computer screen or control console used for monitoring, validation and analysis for a process control system. - View Dependent Claims (48, 49, 50, 51, 52, 53, 54, 55, 56, 57)
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58. A method of monitoring, validation and analysis for a process control system having normal process data and sensors, and represented by a plurality of primary process models and a plurality of process variables, comprising:
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generating a plurality of primary residual process models derived from said primary process models, said normal process data, and one or more statistical parameters corresponding to said normal process data; automatically translating said primary residual process models into executable pseudo-code; measuring real-time sensor data; executing said pseudo-code to compute primary residual values corresponding to said real-time sensor data; deriving an expected value for one or more of said primary residual values from historical normal process data; determining deviations of said primary residual values from said corresponding expected values; mapping one or more of said deviations into one or more compressed values; computing a certainty factor for a possible fault by executing said pseudo-code; and displaying said certainty factor on a monitor, computer screen or control console used for monitoring, validation and analysis for a process control system. - View Dependent Claims (59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70)
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71. A computer-readable medium having computer-executable instructions for performing a method for a process control system having normal process data and sensors, and represented by a plurality of primary process models and a plurality of process variables, said method comprising:
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generating a plurality of primary residual process models derived from said primary process models, said normal process data, and one or more statistical parameters corresponding to said normal process data; measuring real-time sensor data; computing primary residual values of said primary residual process models corresponding to said real-time sensor data; deriving an expected value for one or more of said primary residual values from historical normal process data; determining deviations of said primary residual values from said corresponding expected values; mapping one or more of said deviations of said primary residual values from said corresponding expected values into one or more compressed values; automatically generating one or more fuzzy logic diagnostic rules; computing a certainty factor for a possible fault by applying said fuzzy logic diagnostic rules to said deviations; and displaying said certainty factor on a monitor, computer screen or control console used for monitoring, validation and analysis for a process control system. - View Dependent Claims (72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97)
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98. A computer-readable medium having computer-executable instructions for performing a method for a process control system having normal process data and sensors, and represented by a plurality of primary residual process models and a plurality of process variables, said method comprising:
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measuring real-time sensor data; computing primary residual values of said primary residual process models corresponding to said real-time sensor data; deriving an expected value for one or more of said primary residual values from historical normal process data; determining deviations of said primary residual values from said corresponding expected values; mapping one or more of said deviations of said primary residual values from said corresponding expected values into one or more compressed values; automatically generating one or more fuzzy logic diagnostic rules; computing a certainty factor for a possible fault by applying said fuzzy logic diagnostic rules to said deviations; and displaying said certainty factor on a monitor, computer screen or control console used for monitoring, validation and analysis for a process control system. - View Dependent Claims (99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116)
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Specification