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

• Published in: Chinese journal of mechanical engineering Vol. 30; no. 4; pp. 991 - 1007
• Main Authors: Jing, Ya-Bing, Liu, Chang-Wen, Bi, Feng-Rong, Bi, Xiao-Yang, Wang, Xia, Shao, Kang
• Format: Journal Article
• Language: English
• Published: Beijing Chinese Mechanical Engineering Society 01.07.2017; Springer Nature B.V; State Key Laboratory of Engines, Tianjin University,Tianjin 300072, China; Internal Combustion Engine Research Institute, Tianjin University, Tianjin 300072, China%State Key Laboratory of Engines, Tianjin University,Tianjin 300072, China%School of Mechanical Engineering, Tianjin University,Tianjin 300072, China
• Edition: English ed.
• Subjects: Acceleration; Automotive parts; Classification; Cylinder heads; Diesel engines; Electrical Machines and Networks; Electronics and Microelectronics; Engine valves; Engineering; Engineering Thermodynamics; Engines; Entropy; FastICA算法; Fault diagnosis; Feature extraction; Fractals; Heat and Mass Transfer; Instrumentation; Machines; Manufacturing; Mechanical Engineering; Original Article; Power Electronics; Processes; Theoretical and Applied Mechanics; Valve trains; Vibration analysis; Wavelet analysis; 基于特征; 提取技术; 支持向量机算法; 故障诊断技术; 柴油机故障; 气门间隙; 表面振动信号; Feature extraction; Support vector machine; Diesel engine valve train; FastICA; PCA
• ISSN: 1000-9345; 2192-8258
• DOI: 10.1007/s10033-017-0140-2

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.