哈尔滨工程大学学报Issue(2)：245-249,5.DOI:10.3969/j.issn.1006-7043.201303044
基于P SO-RVM算法的发动机故障诊断
Engine fault diagnosis method based on PSO-RVM algorithm
摘要
Abstract
To solve the problems of the misfiring errors of an automobile engine, the authors, put forward a new in-telligent fault diagnosis method. A mapping relation is established the volume fraction of gases in the exhaust of the automobile and the cause of the misfire. Machine training is applied to normalized data and the trained relevance vector machine model is applied to the fault classification and diagnosis. The penalty factor and the RBF kernel pa-rameters in the algorithm greatly affect the classification accuracy. The particle swarm algorithm is used to optimize the super-parameters;in addition, the relevance vector machine model having experienced optimization training is compared with the presently mature genetic optimized neural network and support vector machine method. The ex-perimental results show that the new method improves the diagnosis accuracy and robustness.关键词
机器学习/相关向量机/核函数/粒子群优化/故障诊断Key words
machine learning/relevance vector machine/kernel function/particle swarm optimization/engine fault diag-nosis分类
信息技术与安全科学引用本文复制引用
毕晓君,柳长源,卢迪..基于P SO-RVM算法的发动机故障诊断[J].哈尔滨工程大学学报,2014,(2):245-249,5.基金项目
国家自然科学基金资助项目（61175126）；中央高校基本科研业务费专项资金资助项目（ HEUCFZ1209）；教育部博士点基金资助项目（20112304110009）． （）
