Fault diagnosis of reciprocating compressor air valve based on VMD-MSE and SSA-SVM

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Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (19) : 289-295.

BIE Fengfeng1,2, ZHU Hongfei1,2, PENG Jian1,2, ZHANG Ying1,2

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Abstract

The fault vibration signal of reciprocating compressor gas valve contains strong nonlinearity and non-stationarity. In order to extract fault features from the vibration signal of the reciprocating compressor valve for fault diagnosis, a fault feature extraction method based on Variational Mode Decomposition(VMD) and Multiscale Sample Entropy(MSE) was proposed, and the model of Support Vector Machine(SVM) optimized by Sparrow Search Algorithm(SSA) was studied for reciprocating compressor valve fault mode recognition. Through the VMD decomposition, the appropriate Intrinsic Mode Function (IMF) component of the vibration signal from the reciprocating compressor valve was selected for signal reconstruction, from which the MSE entropy value was obtained to form a feature vector set. Finally it was input into SSA-SVM for training and mode identification. Experimental study showed that the fault diagnosis model based on VMD-MSE and SSA-VMD can effectively and accurately identified the fault of the reciprocating compressor valve.
Keywords: reciprocating compressor; Variational Modal Decomposition; Multiscale Sample Entropy; Support Vector Machine; mode recognition

Key words

reciprocating compressor / Variational Modal Decomposition / Multiscale Sample Entropy / Support Vector Machine / mode recognition

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BIE Fengfeng1,2, ZHU Hongfei1,2, PENG Jian1,2, ZHANG Ying1,2. Fault diagnosis of reciprocating compressor air valve based on VMD-MSE and SSA-SVM[J]. Journal of Vibration and Shock, 2022, 41(19): 289-295

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