基于KPCA与马氏距离的达林顿管故障预测

为了对达林顿管进行故障预测,提出了基于KPCA与马氏距离的达林顿管故障预测方法。通过对达林顿管进行失效机理分析,设计了加速退化试验,并获取了集电极导通电流与饱和压降性能退化数据,利用小波包分解与核主成分分析进行数据处理,滤除了原始数据中的干扰信号,得到了退化数据的主成分,结合马氏距离对处理后的数据进行特征融合,得到了可以表征达林顿管健康状态变化的健康因子。使用2种故障预测算法对健康因子进行预测,故障预测结果验证了文中方法的有效性,预测值与真实值的误差均在10%以内。

A Darlington Transistor Fault Prognostics Method Based on KPCA and Mahalanobis Distance

In order to predict the failure of Darlington transistor, a method for fault prognostics based on KPCA and Mahalanobis distance is proposed. Through the failure mechanism analysis and accelerated degradation testing of Darlington transistor, the degradation data of collector current and saturation voltage are obtained. The paper utilizes wavelet packet decomposition and KPCA to process the degradation data and filter out interference signals, obtaining the principal component of the degradation. The Mahalanobis distance is used to fuse these components into health index. And the health index could represent the healthy status of Darlington transistor in changes. Finally, two fault predict algorithms are used to predict the HI. And the availability is proved by the forecasting. The results show that RMS between the predicted value and the true value is within 10%.