MFCC and CELP to detect turbine engine faults
First Claim
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1. A fault detection and diagnosis system for a gas turbine engine, the system comprising:
- a sensor positioned on the gas turbine engine; and
a health monitoring unit which receives and processes a sensor signal from the sensor, the health monitoring unit comprising;
a digital storage medium containing a library of fault and non-fault feature profiles mapping fault states and non-fault states of the gas turbine engine, respectively, to combinations of Mel-Frequency Cepstral Coefficient (MFCC) frequency bank energies and/or Code Excited Linear Prediction (CELP) polynomial coefficients; and
a processor which extracts a feature set from the sensor signal using at least one of MFCC algorithms and CELP algorithms, and detects and diagnoses faults in the gas turbine engine by matching the feature set to one or more of the fault or non-fault feature profiles.
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Abstract
A fault detection and diagnosis method for a gas turbine engines comprises collecting a sensor signal from an acoustic or vibrational sensor at the gas turbine engine, pre-processing the sensor signal to remove predictable background, and extracting a feature set from the sensor signal using Mel-Frequency Cepstral Coefficients (MFCC) algorithms and/or Code Excited Linear Prediction (CELP) algorithms. Fault and non-fault states are reported based on comparison between the feature set and a library of fault and non-fault feature profiles corresponding to fault and non-fault states of the gas turbine engine.
16 Citations
20 Claims
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1. A fault detection and diagnosis system for a gas turbine engine, the system comprising:
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a sensor positioned on the gas turbine engine; and a health monitoring unit which receives and processes a sensor signal from the sensor, the health monitoring unit comprising; a digital storage medium containing a library of fault and non-fault feature profiles mapping fault states and non-fault states of the gas turbine engine, respectively, to combinations of Mel-Frequency Cepstral Coefficient (MFCC) frequency bank energies and/or Code Excited Linear Prediction (CELP) polynomial coefficients; and a processor which extracts a feature set from the sensor signal using at least one of MFCC algorithms and CELP algorithms, and detects and diagnoses faults in the gas turbine engine by matching the feature set to one or more of the fault or non-fault feature profiles. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A fault detection and diagnosis method for a gas turbine engine, the method comprising:
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collecting a sensor signal from an acoustic or vibrational sensor at the gas turbine engine; pre-processing the sensor signal to remove predictable background; extracting a feature set from the sensor signal using Mel-Frequency Cepstral Coefficients (MFCC) algorithms and Code Excited Linear Prediction (CELP) algorithms; comparing the feature set to a library of fault and non-fault feature profiles that map fault and non-fault states of the gas turbine engine to combinations of MFCC frequency bank energies and/or CELP polynomial coefficients; and reporting fault and non-fault states of the gas turbine engine based on comparison between the feature set and the feature profiles. - View Dependent Claims (9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20)
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Specification