1.Beijing Key Laboratory of High-end Mechanical Equipment Health Monitoring and Self-Recovery, Beijing University of Chemical Technology, Beijing 100029, China;
2.Key Lab of Engine Health Monitoring-Control and Networking of Ministry of Eduation, Beijing University of Chemical Technology, Beijing 100029, China
Abstract：A novel feature extraction method based on variational mode decomposition (VMD) and singular value decomposition (SVD) is proposed to effectively extract valve clearance fault characteristics from cylinder head surface vibration signals of diesel engine. The vibration signal of cylinder head was decomposed by VMD algorithm, and the modal component was used to construct the characteristic matrix. Then the SVD theory was applied to transform the eigenmatrix into a singular value sequence representing the frequency characteristics. Since the influence of changes in working conditions such as rotating speed and load on the signal characteristic layer might be very similar to the signal characteristic changes caused by faults, the sequence of singular values was taken as characteristic parameters and input into the random forest classifier to build a classification model for the diagnosis of valve clearance faults under variable working conditions of diesel engine. Experimental results show that this method can effectively identify valve clearance faults and highlight fault sensitive features. Compared with the traditional SVD feature extraction method based on Hankel matrix and wavelet packet coefficient matrix, the feature parameters proposed by this method have higher recognition rate under variable working conditions of diesel engine.
江志农1，魏东海1，张进杰2，茆志伟2. 基于VMD和SVD的柴油机气门间隙异常特征提取研究[J]. 振动与冲击, 2020, 39(16): 23-30.
JIANG Zhinong1，WEI Donghai1，ZHANG Jinjie2，MAO Zhiwei2. A study on valve clearance anomaly feature extraction of diesel engines based on VMD and SVD. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(16): 23-30.
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