基于Bayes的超球分类器及在柴油机异常检测中的应用

Bayes-based Hyper-sphere Classification and Its Application in Diesel Engine Abnormity Detection

针对机械故障诊断面临的数据样本分布不均衡和特征空间存在混叠而导致故障诊断正确率不高的问题,提出一种三层超球面分类器异常检测模型,将存在混叠的特征空间分离成正域、负域和混叠域,并针对混叠域样本的判别问题,提出一种基于Bayes的判别超球面确定方法,该方法充分利用样本分布的距离测度和概率密度分布信息,在Bayes准则下使得分类错误概率最小,解决了混叠域样本的分类问题。柴油机实测振动信号的应用实例验证了基于Bayes的判别超球面确定方法能够提高故障诊断的精度。

In view of the low accuracy of mechanical fault diagnosis caused by imbalance data sample distribution and mixed characteristic space,a three-layer hyper-sphere classifier abnormity detection model is proposed,which separates the characteristic space into positive field,negative field and mixed field.In order to decide the labels of samples in mixed field,a definitive method of discrimination hyper-plane based on Bayes is proposed,which uses the samples distribution information of distance and probability density.The samples in mixed field are classified according to Bayes criterion,so as to minimize the classification error probability.The method is applied to the analysis of diesel engine cylinder cover vibration signal,and the result shows that it can improve fault diagnosis accuracy.