United Technologies Corporation

University of Southern California

Inha University

Martin Gawecki

Andrew F Geib

Chung Chieh Kuo

Paul Raymond Scheid

Allan J Volponi

Wonik Choi

EnShuo Tsau

Je Won Kang

Chung Chiech Kuo

System and methods for detecting anomalies and identifying faults of a gas turbine engine may include a recorder in communication with a processor. The recorder may be configured to capture archival data of the gas turbine engine. A flight normalizer module may be configured to produce normalized results based on the archival data. A flight parameter features module may be configured to generate flight parameter features based on the normalized results. A data warehouse module may be configured to determine suspected fault classes by comparing the flight parameter features against training parameter features stored in the data warehouse module based on queries from the flight parameter features module. A majority vote module may be configured to determine a diagnosed fault class based on the suspected fault classes.

Gas turbine engine anomaly detections and fault identifications

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.

MFCC and CELP to detect turbine engine faults

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Los Angeles, CA, United States of America

Martin Gawecki