Diesel Engine Valve Clearance Fault Diagnosis Based on Features Extraction Techniques and FastICA-SVM

作     者：Ya-Bing Jing Chang-Wen Liu Feng-Rong Bi Xiao-Yang Bi Xia Wang Kang Shao 

作者机构：State Key Laboratory of Engines Tianjin UniversityTianjin 300072 China School of Mechanical Engineering Tianjin UniversityTianjin 300072 China Internal Combustion Engine Research Institute Tianjin University Tianjin 300072 China 

出 版 物：《Chinese Journal of Mechanical Engineering》 (中国机械工程学报（英文版）)

年 卷 期：2017年第30卷第4期

页      面：991-1007页

核心收录：

学科分类：080703[工学-动力机械及工程] 08[工学] 0807[工学-动力工程及工程热物理] 

基　　金：Supported by National Science and Technology Support Program of China(Grant No.2015BAF07B04) 

主　　题：Feature extraction Diesel engine valve train FastlCA PCA Support vector machine 

摘      要：Numerous vibration-based techniques are rarely used in diesel engines fault diagnosis in a direct way, due to the surface vibration signals of diesel engines with the complex non-stationary and nonlinear time-varying fea- tures. To investigate the fault diagnosis of diesel engines, fractal correlation dimension, wavelet energy and entropy as features reflecting the diesel engine fault fractal and energy characteristics are extracted from the decomposed signals through analyzing vibration acceleration signals derived from the cylinder head in seven different states of valve train. An intelligent fault detector FastICA-SVM is applied for diesel engine fault diagnosis and classification. The results demonstrate that FastlCA-SVM achieves higher classification accuracy and makes better general- ization performance in small samples recognition. Besides, the fractal correlation dimension and wavelet energy and entropy as the special features of diesel engine vibration signal are considered as input vectors of classifier FastlCA- SVM and could produce the excellent classification results. The proposed methodology improves the accuracy of fea- ture extraction and the fault diagnosis of diesel engines.