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    • Diagnosis Method for Typical Faults of Diesel Engines Based on Multi-Source Information Fusion
    DOI：
    Key Words:diesel engine  multi-source information fusion  t-distributed stochasticneighbor embedding（t-SNE）  fault diagnosis
    Author NameAffiliationE-mail
    LI Baoyue School of Naval Architecture， Ocean and Energy Power Engineering， Wuhan University of Technology， Wuhan 430063， China li.by@whut.edu.cn 
    YU Yonghua* School of Naval Architecture， Ocean and Energy Power Engineering， Wuhan University of Technology， Wuhan 430063， China yyhua@whut.edu.cn 
    CAO Bingxin School of Naval Architecture， Ocean and Energy Power Engineering， Wuhan University of Technology， Wuhan 430063， China bxcao@whut.edu.cn 
    YE Jianping School of Naval Architecture， Ocean and Energy Power Engineering， Wuhan University of Technology， Wuhan 430063， China 2325808287@qq.com 
    MA Bingjie Key Laboratory of Marine Power Engineering and Technology of the Ministry of Transport， Wuhan 430063， China  
    YAO Yangfeng School of Naval Architecture， Ocean and Energy Power Engineering， Wuhan University of Technology， Wuhan 430063， China yaoyangfeng@whut.edu.cn 
    ZHAO Guoxu School of Naval Architecture， Ocean and Energy Power Engineering， Wuhan University of Technology， Wuhan 430063， China zgx1049722103919@whut.edu.cn 
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    Abstract:A fault diagnosis method based on t-distributed stochastic neighbor embedding（t-SNE） and multi-source information fusion was proposed because it is difficult to distinguish the faults of different components of diesel engines based on a single vibration signal. The fault simulation model of the diesel engine was calibrated through experiments. Based on the simulation model， the thermal parameters and cylinder head vibration under different fault conditions were obtained. Thermal parameters with low correlation were selected， and the time domain and frequency domain characteristic parameters of the vibration signal were extracted. The vibration characteristic parameters and thermal parameters were fused and dimensionally reduced using t-SNE. The data after dimensional reduction was classified and recognized based on the support vector machine（SVM） method to construct a fault diagnosis model for the diesel engine， and a fault recognition accuracy of 95.7% was finally achieved. Compared with the fault diagnosis method based on a single vibration signal， multi-source information fusion can effectively distinguish different fault categories and improve the fault recognition accuracies of the diesel engine.
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