基于运行工况和多分类支持向量机的柴油机共轨系统诊断方法

Diagnosis Method of Diesel Common Rail System Based on Operating Conditions and Multi-Category Support Vector Machine

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.