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%.
蒋佳炜1,胡以怀1,柯赟2,陈彦臻1. 基于小波包特征提取和模糊熵特征选择的柴油机故障分析[J]. 振动与冲击, 2020, 39(4): 273-277.
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. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(4): 273-277.
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