Exception analysis for multimissions
First Claim
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1. A method for diagnosis and prognosis of faults in a physical system comprising:
- receiving sensor data representative of measurements made on the physical system, the measurements being representative of values of signals produced by the physical system;
receiving discrete data comprising system status variables and system command information;
producing model enhanced sensor signals by fitting the sensor data to a partial model of the physical system;
producing predicted system states based on the discrete data;
detecting discrepancies among the discrete data;
identifying suspected bad signals by detecting discrepancies among the sensor data based on a statistical model of the sensor data;
confirming detection of failures based on the discrepancies among the discrete data, on the suspected bad signals, and on the predicted system states; and
producing predicted faults based on a stochastic model of the sensor data to produce predicted values of the sensor data from the stochastic model, the predicted faults being based on the predicted values.
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Abstract
A generalized formalism for diagnostics and prognostics in an instrumented system which can provide sensor data and discrete system variable takes into consideration all standard forms of data, both time-varying (sensor or extracted feature) quantities and discrete measurements, embedded physical and symbolic models, and communication with other autonomy-enabling components such as planners and schedulers. This approach can be adapted to on-board or off-board implementations with no change to the underlying principles.
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Citations
27 Claims
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1. A method for diagnosis and prognosis of faults in a physical system comprising:
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receiving sensor data representative of measurements made on the physical system, the measurements being representative of values of signals produced by the physical system;
receiving discrete data comprising system status variables and system command information;
producing model enhanced sensor signals by fitting the sensor data to a partial model of the physical system;
producing predicted system states based on the discrete data;
detecting discrepancies among the discrete data;
identifying suspected bad signals by detecting discrepancies among the sensor data based on a statistical model of the sensor data;
confirming detection of failures based on the discrepancies among the discrete data, on the suspected bad signals, and on the predicted system states; and
producing predicted faults based on a stochastic model of the sensor data to produce predicted values of the sensor data from the stochastic model, the predicted faults being based on the predicted values. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. The system health monitor of claim I further including identifying a deterministic component in the sensor data, producing a residual component in the sensor data by removing the deterministic component, separating the residual component into a linear component, a non-linear component and a noise component, and fitting the linear component, the non-linear component and the noise component to a stochastic model, wherein the discrepancies among the sensor data are based on the stochastic model.
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9. A system health monitor for diagnosis and prognosis of faults in a physical system being monitored comprising:
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a model filter having at least a partial model representation of the physical system, the model filter operable to produce a plurality of model enhanced signals based on sensor data, the sensor data representative of measurements made on the physical system, the measurements being representative of values of signals produced by the physical system;
a symbolic data model operable to produce predicted system states based on discrete data comprising system status variables and system command information, the symbolic data model further operable to detect discrepancies among the discrete data;
a first anomaly detector operable to detect discrepancies in the sensor data based on a statistical model of the sensor data, the discrepancies in the sensor data constituting suspect bad signals;
a predictive comparator module operable to confirm a failure based on detected discrepancies among the discrete data, the suspected bad signals, and the predicted system states;
a prognostic assessment module operable to produce predicted faults using a stochastic model of the sensor data to produce future values of the sensor data from the stochastic model; and
a presentation module for presenting information relating to the health of the system comprising the detected discrepancies and the predicted faults, the information suitable for a human user or a machine process. - View Dependent Claims (10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 24, 25, 27)
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16. A computer program product for diagnosis and prognosis of faults in a physical system comprising:
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a computer readable medium having contained thereon computer instructions suitable for execution on a computer system, computer instructions comprising;
first executable program code effective to operate the computer system to receive sensor data representative of measurements made on the physical system, the measurements being representative of values of signals produced by the physical system;
second executable program code effective to operate the computer system to receive discrete data comprising system status variables and system command information;
third executable program code effective to operate the computer system to produce model enhanced sensor signals by fitting the sensor data to a partial model of the physical system;
fourth executable program code effective to operate the computer system to produce predicted system states based on the discrete data;
fifth executable program code effective to operate the computer system to detect discrepancies among the discrete data;
sixth executable program code effective to operate the computer system to identify suspected bad signals by detecting discrepancies among the sensor data based on a statistical model of the sensor data;
seventh executable program code effective to operate the computer system to confirm detection of failures based on the discrepancies among the discrete data, on the suspected bad signals, and on the predicted system states; and
eighth executable program code effective to operate the computer system to produce predicted faults based on a stochastic model of the sensor data to produce predicted values of the sensor data from the stochastic model, the predicted faults being based on the predicted values.
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23. A system health monitor for diagnosis and prognosis of faults in a physical system being monitored comprising:
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a model filter having at least a partial model representation of the physical system, the model filter operable to produce a plurality of model enhanced signals based on sensor data, the sensor data representative of measurements made on the physical system;
a symbolic data model operable to produce predicted system states based on discrete data comprising system status variables and system command information, the symbolic data model further operable to detect discrepancies among the discrete data;
a first anomaly detector operable to detect discrepancies in the sensor data based on a statistical model of the sensor data, the discrepancies in the sensor data being identified as suspect bad signals;
a second anomaly detector operable to identify unmodeled events by computing coherence statistics from the sensor data and comparing the coherence statistics against expected coherence quantities indicative of known operating conditions of the physical system;
a predictive comparator module operable to confirm a failure based on detected discrepancies among the discrete data, the suspected bad signals, and the predicted system states, the predictive comparator module further operable to distinguish the unmodeled events from modeled events based on the suspected bad signals and the model enhanced signals;
a prognostic assessment module operable to produce predicted faults using a stochastic model of the sensor data to produce future values of the sensor data from the stochastic model; and
a presentation module for presenting information relating to the health of the system comprising detected discrepancies, a categorization of modeled and unmodeled events, and predicted faults, the information suitable for a human user or a machine process.
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26. A method for diagnosis and prognosis of faults in a physical system being monitored comprising:
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producing a plurality of model enhanced signals based on fitting sensor data to at least a partial model physical model of the physical system, the sensor data representative of measurements made on the physical system;
producing predicted system states based on discrete data comprising system status variables and system command information, the symbolic data model further operable to detect discrepancies among the discrete data;
detecting discrepancies in the sensor data based on a statistical model of only the statistical components of the sensor data, the discrepancies in the sensor data being identified as suspect bad signals;
identifying correlated signal among the sensor data;
identifying unmodeled events by comparing the correlated signals against expected correlated signals which are indicative of known operating conditions of the physical system;
confirming the detection of failures based on detected discrepancies among the discrete data, on the suspected bad signals, and the predicted system states;
producing predicted faults using a stochastic model of the sensor data to produce predicted values of the sensor data from the stochastic model; and
presenting information relating to the health of the system comprising detected discrepancies, a categorization of modeled and unmodeled events, and predicted faults, the information suitable for a human user or a machine process.
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Specification