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

查看全文 作　　者：Ya-Bing [1,3]Jing;Chang-Wen [1]Liu;Feng-Rong [1]Bi;Xiao-Yang [2]Bi;Xia [1]Wang;Kang [1]Shao 高影响力作者 机构地区：[1]State Key Laboratory of Engines, Tianjin University,Tianjin 300072, China;[2]School of Mechanical Engineering, Tianjin University,Tianjin 300072, China;[3]Internal Combustion Engine Research Institute, Tianjin University, Tianjin 300072, China高影响力机构 出　　处：《Chinese Journal of Mechanical Engineering》索引2017年第30卷第4期,共17页高影响力期刊 基　　金：Supported by National Science and Technology Support Program of China(Grant No.2015BAF07B04) 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 features. 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 FastICA-SVM achieves higher classification accuracy and makes better generalization 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 Fast ICASVM and could produce the excellent classification results.The proposed methodology improves the accuracy of feature extraction and the fault diagnosis of diesel engines. 关 键 词：故障诊断技术 柴油机故障 基于特征 提取技术 支持向量机算法 气门间隙 FASTICA算法 表面振动信号
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