分步自回归核主元分析法及其应用

Step Auto-regression Kernel Principal Component Analysis and Its Application

针对传统核主元分析法诊断动态系统故障可能会出现大量误报、漏报问题,提出一种分步自回归核主元分析法。该方法以分步动态策略为基础建立诊断模型,结合滑动窗口机制和指数加权思想,通过不断加入经加权处理的实时数据更新诊断模型,使用T^2和SPE统计量判断系统是否发生故障。将该方法应用于柴油机气阀机构故障检测,结果表明:该方法既能充分利用原始数据和实时动态信息,自动更新模型,又能减小计算量,更早监测出异常工作状态,提高故障诊断的快速性,还能增大故障敏感度,使诊断结果更加准确可靠。

A large number of false positives and false negatives may emerge when using traditional kernel principal component analysis to make fault diagnosis to dynamic systems. To solve the problem, a step auto- regression Kernel Principal Component Analysis （KPCA） algorithm is proposed. The method establishes a fault diagnosis model based on step dynamic strategy. Based on sliding window mechanism and exponential weighting idea, it updates the diagnosis model by continuously adding weighted, real-time data. T2 and SPE statistic are used to detect whether the system has faults or not. The method is applied to fault detection of diesel engine valve, and the results indicate that the algorithm can not only make full use of original data and real-time dynamic information to update the model automatically, but also reduce the calculation cost, detect abnormal working status earlier, and improve the accuracy of fault diagnosis. Furthermore, the method can make diagnosis result more accurate and reliable by increasing the fault sensitivity.