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Articles:2022,Vol:27,Issue(3):144-157 |
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Citation: |
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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 |
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| Fault Identification of Internal Combustion Engine based on Support Vector Machine and Fuzzy Neural Network |
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| CHEN Decheng, HE Xinyu |
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| School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, Shandong, China |
| Abstract: |
| 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
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| Received: 2022-04-08
Revised:
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| DOI: 10.13434/j.cnki.1007-4546.2022.03002 |
| Corresponding author:
Email： |
| Author description:
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References: |
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