Fault feature extraction method of diesel engine cylinder based on AWMMD
WANG Min1, QIN Guojun1,2, LIAO Yifan1
1. College of Information and Mechanical-Electrical Engineering, Hunan International Economics University, Changsha 410072, China;
2. College of Electrical and Information Engineering, Hunan University, Changsha 410072, China
Abstract:Aiming at the noise interference problem in diesel engine cylinder fault diagnosis, an adaptive weighted multi-scale morphological decomposition (AWMMD) method was proposed to extract fault features from vibration signals of each cylinder head surface. Firstly, a new combined difference morphological filter was constructed based on three combination operators, which was used to decompose vibration signals in multi-scale. Secondly, a genetic algorithm based adaptive weight assignment algorithm for each scale morphological pattern component was designed with Teager energy kurtosis as the evaluation index, and a weighted multi-scale morphological decomposition method was proposed. Finally, the adaptive weight and the morphological mode components of multi-scale decomposition were combined to obtain the optimized fault feature extraction results. The results of simulation signal test and diesel engine fault simulation signal analysis show that the proposed method can effectively suppress noise interference and extract fault features.
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