Upgrading engine test cells for improved troubleshooting and diagnostics

Effectively utilizing existing on-board and atwing data that encompasses environmental, built-in-test (BIT), vibration, and maintenance test results, can realize improved maintenance, survivability and critical failure rates via the identification of degrading components prior to critical failure. The authors have developed software to show the feasibility of onboard and at-wing prognostics using field data from an Army aircraft platform as a multifaceted case study for predicting and diagnosing the health of a mission-critical targeting subsystem.

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Research on Fault Diagnosis for Rotating Machinery Vibration of Aero-Engine Based on Wavelet Transformation and Probabilistic Neural Network

Huang

2011

AMR

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Fault feature extraction using wavelet decomposition and probabilistic neural network fault diagnosis technology is presented in this paper. Fault diagnosis based on wavelet transformation and neural network data fusion is studied. The fault diagnosis in rotating machinery vibration of the aero-engine is simulated in Matlab. Our recent investigations demonstrate that using wavelet decomposition extract fault characteristics of the energy vector has strong generalization ability and anti-noise ability. Integration of the wavelet and neural network application can provide a better classification of diagnosis results, reliability and accuracy. This technique is suitable for the mechanical vibration fault diagnosis applications of steam turbine and gas turbine.

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Upgrading engine test cells for improved troubleshooting and diagnostics

Cited by 4 publications

References 6 publications

Advanced vibration diagnostics for engine test cells

Advanced vibration diagnostics for engine test cells

Field Data Evaluation and Continuous Health Assessment of Critical Avionics Subsystem Degradation

Research on Fault Diagnosis for Rotating Machinery Vibration of Aero-Engine Based on Wavelet Transformation and Probabilistic Neural Network

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