METHOD AND SYSTEM OF MONITORING PROGNOSTICS
First Claim
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1. A method comprising:
- sensing periodic time based indicia;
sensing a plurality of sets of conditions associated with operation of a selected system at spaced apart timed intervals;
generating representations of the sensed conditions;
processing the representations along with at least some of the time based indicia, with at least one neural network with the network exhibiting a plurality of learned states with each state corresponding to a respective set of sensed conditions; and
combining results of the processing, and responsive to the combining,producing at least one of an alarm indicator, or, a control signal,where processing includes comparing time evolution of sensed conditions with learned time evolution of operation of the selected system.
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Abstract
A neural network learns the operating modes of a system being monitored under normal operating conditions. Anomalies can be automatically detected and learned. A control command can be issued or an alert can be issued in response thereto.
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Citations
39 Claims
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1. A method comprising:
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sensing periodic time based indicia; sensing a plurality of sets of conditions associated with operation of a selected system at spaced apart timed intervals; generating representations of the sensed conditions; processing the representations along with at least some of the time based indicia, with at least one neural network with the network exhibiting a plurality of learned states with each state corresponding to a respective set of sensed conditions; and combining results of the processing, and responsive to the combining, producing at least one of an alarm indicator, or, a control signal, where processing includes comparing time evolution of sensed conditions with learned time evolution of operation of the selected system. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9-23. -23. (canceled)
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24. A method comprising:
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providing a learning mode to train at least one neural network in the expected, normal, operation of a movable vehicle; sensing a plurality of vehicle inputs, the values of which are indicative of normal operation of the vehicle, and responsive thereto establishing an expected, normal operational profile of the vehicle in the at least one neural network; providing a detection mode for the at least one neural network; and sensing the plurality of vehicle inputs, the values of which are indicative of real-time vehicle operation and, responsive thereto, determining, using the at least one neural network and the previously established normal operational profile, if the present values are indicative of normal operation of the vehicle. - View Dependent Claims (25, 26, 27, 28, 29, 30, 31, 32, 33)
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34. A method comprising:
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providing a learning mode to train at least one neural network in the expected, normal, behavior of a selected entity; sensing a plurality of time varying inputs associated with the expected behavior of the entity, and responsive thereto establishing an expected, normal behavior profile of the entity in the at least one neural network; providing a sequential plurality of time based indicia; providing a detection mode for the at least one neural network; and sensing the plurality of time varying inputs, the values of which are indicative of real-time behavior of the entity, and the time based indicia, and, responsive thereto, determining, using the at least one neural network and the previously established normal behavior profile, if the present values are indicative of expected, normal behavior of the entity. - View Dependent Claims (35, 36, 37, 38, 39)
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Specification