Fault diagnosis of diesel engines based on wavelet packet energy spectrum feature extraction and fuzzy entropy feature selection

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Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (4) : 273-277.

JIANG Jiawei1， HU Yihuai1， KE Yun2， CHEN Yanzhen1

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Abstract

Ship power equipment makes fault pattern recognition and state prediction more difficult due to few samples, slow changes, and the nonlinear structure of data of fault monitoring signals.Especially for the diesel engine vibration signal fault diagnosis, due to the noise of the diesel engine vibration signal, the diagnosis signal is difficult in feature selection.This paper presents a diesel engine fault diagnosis method based on wavelet packet energy spectrum feature extraction and fuzzy entropy feature selection.The feature set extracted from the wavelet packet energy spectrum is selected by fuzzy entropy, and the selected feature parameters are input to the LS-SVM for fault pattern recognition.Experimental results show that this method can improve the accuracy of fault recognition.In the experiment of this paper, the fault recognition accuracy rate reaches 99.36%.Compared with the feature set without feature selection, the recognition accuracy rate is increased by 0.72%.

Key words

wavelet packet analysis / fuzzy entropy / feature selection / support vector machine / diesel engine fault diagnosis / fault pattern recognition

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JIANG Jiawei1， HU Yihuai1， KE Yun2， CHEN Yanzhen1. Fault diagnosis of diesel engines based on wavelet packet energy spectrum feature extraction and fuzzy entropy feature selection[J]. Journal of Vibration and Shock, 2020, 39(4): 273-277

References

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