Fault detection system and method using approximate null space base fault signature classification
First Claim
1. A fault detection system for detecting faults in a turbine engine, the fault detection system comprising:
- a neural network system, the neural network system adapted to receive sensor data from the turbine engine and generate a plurality of approximate null space scores that represent discrimination features in the sensor data; and
a discriminant based classifier, the discriminant based classifier adapted to receive the approximate null space scores and classify the approximate null space scores to determine a likelihood of fault in the turbine engine.
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Abstract
A system and method for fault detection is provided. The fault detection system provides the ability to detect symptoms of fault in turbine engines and other mechanical systems that have nonlinear relationships between two or more variables. The fault detection system uses a neural network to perform feature extraction from data for representation of faulty or normal conditions. The values of extracted features, referred to herein as scores, are then used to determine the likelihood of fault in the system. Specifically, the lower order scores, referred to herein as “approximate null space” scores can be classified into one or more clusters, where some clusters represent types of faults in the turbine engine. Classification based on the approximate null space scores provides the ability to classify faulty or nominal conditions that could not be reliably classified using higher order scores.
8 Citations
20 Claims
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1. A fault detection system for detecting faults in a turbine engine, the fault detection system comprising:
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a neural network system, the neural network system adapted to receive sensor data from the turbine engine and generate a plurality of approximate null space scores that represent discrimination features in the sensor data; and a discriminant based classifier, the discriminant based classifier adapted to receive the approximate null space scores and classify the approximate null space scores to determine a likelihood of fault in the turbine engine. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method of detecting fault in a turbine engine, the method comprising the steps of:
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receiving sensor data from the turbine engine; generating a plurality of approximate null space scores from the sensor data, the plurality of approximate null space scores representing discrimination features in the sensor data; and classifying the null space scores based on discrimination to determine a likelihood of fault in the turbine engine. - View Dependent Claims (9, 10, 11, 12, 13)
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14. A program product comprising:
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a) a fault detection program, the fault detection program including; a neural network system, the neural network system adapted to receive sensor data from a turbine engine and generate a plurality of approximate null space scores that represent discrimination features in the sensor data; and a discriminant based classifier, the discriminant based classifier adapted to receive the approximate null space scores and classify the approximate null space scores to determine a likelihood of fault in the turbine engine; and b) computer-readable signal bearing media bearing said program. - View Dependent Claims (15, 16, 17, 18, 19, 20)
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Specification