Fault severity assessment for gears based on AR model and auto-associative neural network

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Journal of Vibration and Shock ›› 2019, Vol. 38 ›› Issue (2) : 18-24.

ZHANG Long1,2,3,CHENG Junliang2,YANG Shixi1,LI Xinglin3

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Abstract

The gear damage severity evaluation underlines the prognostics and condition-based maintenance of mechanical systems.Being motivated by the fact that paradigms based on the probability similarity,like the hidden Markov model (HMM) and Gaussian mixed model (GMM),tend to an early saturation,a new approach for gear damage evaluation was proposed by making use of an autoregressive model (AR) and auto-associative neural network (AANN).The AR model was made to fit with gear vibration signals and the model coefficients were extracted as feature vectors which were then,fed to AANN to obtain reconstructed AR coefficients.A baseline AANN was trained by using the feature vectors from normal condition.The reconstructed AR coefficients by the baseline AANN will deviate from the original AR coefficients,if the gear condition degrade from normal condition.So,the difference between the residuals of AR models using reconstructed and original AR coefficients was exploited to formulate a gear damage indicator.Two experimental data sets involving one discrete damage-degree and one run-to-failure test were utilized to verify the proposed method.The results show the novel indicator is able to track damage progress with a consistent trend and to detect incipient damage in time.

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AR model / Auto-associative neural network / Gears / Fault severity / Prognostics

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ZHANG Long1,2,3,CHENG Junliang2,YANG Shixi1,LI Xinglin3. Fault severity assessment for gears based on AR model and auto-associative neural network[J]. Journal of Vibration and Shock, 2019, 38(2): 18-24

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