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    • Intelligent Diagnosis Method Based on Genetic Algorithm Optimization and Support Vector Machine for Leakage Faults of Gas Valves of Marine Diesel Engines
    DOI：10.13949/j.cnki.nrjgc.2022.02.009
    Key Words:diesel engine  fault diagnosis  valve leakage  genetic algorithm（GA）  support vector machine（SVM）  fault pattern recognition
    Author NameAffiliationE-mail
    CAI Yijie* School of Mechanical Engineering， Hubei University of Technology， Wuhan 430068， China
    Hubei Key Laboratory of Modern Manufacturing Quality Engineering， Hubei University of Technology， Wuhan 430068， China
    School of Energy and Power Engineering， Wuhan University of Technology， Wuhan 430070， China     		caiyijiecyj@163.com 
    CHEN Junjie School of Mechanical Engineering， Hubei University of Technology， Wuhan 430068， China  
    WANG Jun School of Mechanical Engineering， Hubei University of Technology， Wuhan 430068， China  
    ZHANG Yundong A Direct Branch of China Coast Guard， Sanya 572000， China  
    YANG Jianguo School of Energy and Power Engineering， Wuhan University of Technology， Wuhan 430070， China  
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    Abstract:To solve the problem of valve leakage on marine diesel engines， a diagnosis method combining genetic algorithm（GA） and support vector machine（SVM） was proposed and named as GA-SVM. Through the analysis of cylinder head vibration signal in stable states and working states， the characteristic parameters for SVM model training were extracted and the faults were identified by the penalty factors and kernel function parameters of GA-SVM. Results show that the GA-SVM method improves the selection of SVM parameters， and the recognition method for valve leakage fault is effective. The overall fault diagnosis accuracy rate after optimization is 99.333%， which is about 2% higher than that before optimization.
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