Application of wavelet energy and Shannon entropy for feature extraction in gearbox fault detection under varying speed conditions

作者：Bafroui Hojat Heidari; Ohadi Abdolreza^*

来源：Neurocomputing, 2014, 133: 437-445.

DOI：10.1016/j.neucom.2013.12.018

• Non-stationary vibration
• Gearbox
• Wavelet
• Shannon entropy
• Neural network
• Fault detection

The fault detection and feature extraction of varying speed machinery with multi-component signals are full of difficulties caused by non-stationary machine dynamics and vibrations. In monitoring the vibrations of varying speed machinery, mainly formal signal processing methods based on digital sampling accomplished in equal time intervals become unsuitable. On the other hand, energy and Shannon entropy distribution of gear vibration signals measured in time-frequency plane would be different from the distribution under the normal state, when faults occur in the gear. Therefore, it is possible to detect a fault by comparing the energy and Shannon entropy distribution of gear vibration signals with and without fault conditions. In this paper, for fault diagnosis of gearbox in the run-up condition, primarily the obtained vibration signals from an acceleration sensor of automotive gearbox test setup are sampled at constant time increment by an acquisition card. To process the non-stationary vibration signals, the re-sampling technique at constant angle increment is combined with the continuous wavelet transform (CWT) and the wavelet coefficients of the signals are obtained. The Monet wavelet is used; because impulses in many mechanical dynamic signals are always the indication of faults and the Morlet wavelet is exceedingly comparable to an impulse component. Then, statistical parameters of the wavelet coefficients are extracted that constitute the feature vectors. As a new method, the optimal range of wavelet scales is selected based on the maximum energy to Shannon entropy ratio criteria and consequently feature vectors are reduced. In addition, energy and Shannon entropy of the wavelet coefficients are used as two new features along other statistical parameters as input of the classifier. Finally, a feed-forward multilayer perceptron (MLP) neural network uses the extracted features for classification. The experimental results show that the presented method can diagnose the faults of the gear chip and wear efficiently.

• 出版日期2014-6-10