Method for Building Health Index of Darlington Transistor Based on KPCA and Mahalanobis Distance

Abstract

In order to obtain characteristic parameters which can describe the healthy status of Darlington transistor, a method for building Health Index (HI) based on KPCA and Mahalanobis distance was proposed. Through the failure mechanism analysis and Accelerated Degradation Testing (ADT) of Darlington transistor, the degradation data of collector current and saturation voltage were obtained. For the degradation data has obvious nonlinear features and some noise which are unfavorable for failure analysis, a data processing method was put forward by using wavelet packet decomposition and Kernel Component Analysis (KPCA). The interference signal was filtered and the main components were obtained. By using this method. Finally, the Mahalanobis distance was used to fuse these components into Health Index. And the Health Index could represent how the healthy status of Darlington transistor changes. Through multiple sets of data verification, the Health Index used Mahalanobis distance showed higher prediction accuracy than the Euclidean distance with the same fault predict algorithm.