CHEN Decheng, HE Xinyu. Fault Identification of Internal Combustion Engine based on Support Vector Machine and Fuzzy Neural Network[J]. International Journal of Plant Engineering and Management, 2022, 27(3): 144-157

Fault Identification of Internal Combustion Engine based on Support Vector Machine and Fuzzy Neural Network
CHEN Decheng, HE Xinyu
School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, Shandong, China
The internal combustion engine is the main power source of current large-scale machinery and equipment. Overhaul and maintenance of its faults are important conditions for ensuring the safe and stable operation of machinery and equipment, and the identification of faults is a prerequisite. Therefore, the fault identification of internal combustion engines is one of the important directions of current research. In order to further improve the accuracy of the fault recognition of internal combustion engines, this paper takes a certain type of internal combustion engine as the research object, and constructs a support vector machine and a fuzzy neural network fault recognition model. The binary tree multi-class classification algorithm is used to determine the priority, and then the fuzzy neural network is verified. The feasibility of the model is proved through experiments, which can quickly identify the failure of the internal combustion engine and improve the failure processing efficiency.
Key words:    internal combustion engine    support vector machine    fuzzy neural network    fault recognition   
Received: 2022-04-08     Revised:
DOI: 10.13434/j.cnki.1007-4546.2022.03002
Corresponding author:     Email:
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CHEN Decheng
HE Xinyu

[1] FENG H Z, PENG D, YUAN R D. Research on engine fault diagnosis based on PSO-SVM[J]. Computer Measurement and Control, 2014,22(2):355-357+360 (in Chinese)
[2] CHEN J. Research on diesel engine fault diagnosis technology based on support vector machine[D]. Wuhan:Wuhan University of Technology, 2012 (in Chinese)
[3] CAI Y P, ZHANG H, SHI L S, et al. Internal combustion engine fault recognition based on improved binary tree support vector machine[J]. Transactions of CSICE, 2019,37(4):367-373 (in Chinese)
[4] SONG H Y, JI W, LI B. Research on engine fault diagnosis expert system based on fuzzy neural network[J]. Internal Combustion Engine & Power Equipment, 2004,(5):9-11 (in Chinese)
[5] ZHANG F Y. Research on typical fault identification system and method of internal combustion engine based on fbg acceleration sensor[D]. Shandong:Shandong University, 2017 (in Chinese)
[6] WU Z Y, YUAN H Q, LI L. Internal combustion engine fault diagnosis based on chaotic feature and support vector machine[J]. Journal of Mechanical Strength, 2010,32(5):723-728 (in Chinese)
[7] CAI Y P, FAN Y, CHEN W, et al. Application of improved time-frequency analysis and feature fusion in fault diagnosis of internal combustion engine[J]. China Mechanical Engineering, 2020,31(16):1901-1911 (in Chinese)
[8] ZHANG Z. Research on power plant fault diagnosis of heating boiler house based on neural network and support vector machine and its improved algorithm[D]. Taiyuan:Taiyuan University of Technology, 2016 (in Chinese)
[9] DI L U, DOU W J. Fault diagnosis of engine misfire based on genetic optimized support vector machine[J]. Journal of Harbin University of Science and Technology, 2012
[10] ZHANG F, JIANG M, ZHANG L, et al. Internal combustion engine fault identification based on fbg vibration sensor and support vector machines algorithm[J]. Mathematical Problems in Engineering, 2019, 2019(4):1-11