摘要
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。
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%.
作者
刘强
程进军
谭洋波
郭文浩
李剑峰
LIU Qiang;CHENG Jinjun;TAN Yangbo;GUO Wenhao;LI Jianfeng(Aeronautics Engineering College,Air Force Engineering University,Xi＇an 710038,China)
出处
《空军工程大学学报（自然科学版）》
CSCD
北大核心
2018年第5期71-77,共7页
Journal of Air Force Engineering University(Natural Science Edition)
基金
国家自然科学基金(51779263)
关键词
故障预测
达林顿管
核主成分分析
马氏距离
健康因子
fault prognostics
Darlington transistor
kernel principal component analysis
Mahalanobis distance
health index
