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作 者:黄英[1] 王拓 裴海俊 王健 王绪 HUANG Ying;WANG Tuo;PEI Haijun;WANG Jian;WANG Xu(School of Mechanical Engineering,Beijing Institute of Technology,Beijing 100081,China)
机构地区:[1]北京理工大学机械与车辆学院,北京100081
出 处:《北京理工大学学报》2023年第7期719-725,共7页Transactions of Beijing Institute of Technology
基 金:国家自然科学基金资助项目(51475043,50975026)。
摘 要:对于柴油机共轨系统计量阀复位弹簧松弛和喷油器针阀偶件磨损两类典型故障,提出了基于运行工况和多分类支持向量机的故障诊断策略.该策略考虑到运行工况对故障诊断精度的影响,根据车速将实车采集数据划分为3个子工况集.通过卡方检验和机理分析选择和故障情况相关度较高的状态参数,并使用主成分分析法提取特征参数,并根据轮廓系数筛选出对故障敏感度最高的子工况集.采用层次定比采样方法划分训练集,通过粒子群算法优化支持向量机的惩罚参数和径向基函数参数.最后通过实车测试数据集对模型进行验证,实验结果表明该方法在在突出工况的诊断正确率基本达到90%以上,满足故障诊断要求.A fault diagnosis strategy based on operating conditions and multi-classification support vector machine was proposed for two typical faults of diesel common rail system with loose metering valve reset spring and worn injector needle valve coupling.The strategy was arranged as follows.Firstly,the influence of operating conditions on the fault diagnosis accuracy was taken into account and the data collected from real vehicles were divided into three sub-conditions according to the vehicle speed.Then the state parameters correlated highly with the fault condition were selected by cardinality test and mechanism analysis,and the feature parameters were extracted using principal component analysis,and the sub-condition set with the highest sensitivity to the fault was filtered according to the contour coefficient S.A hierarchical fixed-ratio sampling method was used to divide the training set,and the penalty parameter c and radial basis function(RBF)parameter g of the support vector machine were optimized by the particle swarm algorithm.Finally,the model was validated by using a real vehicle test data set.The experimental results show that the correct diagnosis rate of the method in the prominent working conditions can reach more than 90%,which meets the fault diagnosis requirements.
关 键 词:共轨系统 故障诊断 支持向量机 工况划分 主成分分析 粒子群算法
分 类 号:TK428[动力工程及工程热物理—动力机械及工程]
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