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

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Abstract

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 FastICA-SVM and could produce the excellent classification results. The proposed methodology improves the accuracy of feature extraction and the fault diagnosis of diesel engines.

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Authors and Affiliations

  1. State Key Laboratory of Engines, Tianjin University, Tianjin, 300072, China

    Ya-Bing Jing, Chang-Wen Liu, Feng-Rong Bi, Xia Wang & Kang Shao

  2. School of Mechanical Engineering, Tianjin University, Tianjin, 300072, China

    Xiao-Yang Bi

  3. Internal Combustion Engine Research Institute, Tianjin University, Tianjin, 300072, China

    Ya-Bing Jing

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  1. Ya-Bing Jing
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  2. Chang-Wen Liu
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  3. Feng-Rong Bi
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  4. Xiao-Yang Bi
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  5. Xia Wang
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Corresponding author

Correspondence to Feng-Rong Bi.

Additional information

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

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Jing, YB., Liu, CW., Bi, FR. et al. Diesel Engine Valve Clearance Fault Diagnosis Based on Features Extraction Techniques and FastICA-SVM. Chin. J. Mech. Eng. 30, 991–1007 (2017). https://doi.org/10.1007/s10033-017-0140-2

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  • DOI: https://doi.org/10.1007/s10033-017-0140-2

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