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TECHNICAL PAPERS: Gas Turbines: Controls, Diagnostics, and Instrumentation

Setting Up of a Probabilistic Neural Network for Sensor Fault Detection Including Operation With Component Faults

[+] Author and Article Information
C. Romesis, K. Mathioudakis

Laboratory of Thermal Turbomachines, National Technical University of Athens, Athens 15710, Greece

J. Eng. Gas Turbines Power 125(3), 634-641 (Aug 15, 2003) (8 pages) doi:10.1115/1.1582493 History: Received December 01, 2001; Revised March 01, 2002; Online August 15, 2003
Copyright © 2003 by ASME
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References

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Figures

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Performance of the PNN on sensor fault detection on a drifting deteriorated turbofan
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Performance of the PNN on sensor fault detection on a turbofan with multiple component faults
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Minimum detectable levels of XNHP sensor fault, for several levels of HP turbine fault (SW41 deviation)
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Performance of the PNN on detection of sensor’s XNHP and WFE fault on a turbofan with single-component fault
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PNN success rate for cases of multiple sensor faults
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Procedure followed for multiple sensor fault detection
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Detection of a drift in sensor P13: (a) time variation of P13, (b) probabilities produced by the network
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Minimum detectable levels of sensor faults in terms of measurement standard deviations
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Minimum detectable levels of sensor faults
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PNN output for several levels of T3 sensor fault
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PNN performance matrix for sensor fault detection
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The procedure for evaluating sensor fault cases
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The procedure for generating the sets of patterns
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Patterns of deltas for different situations: (a) healthy engine and sensors, (b) faulty engine, healthy sensor, (c) healthy engine, faulty sensor (XNLP), (d) faulty engine and sensor (XNLP)
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Probabilistic neural network architecture for turbofan sensor fault diagnosis
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Layout and station numbering of a turbofan engine

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