Xiangxiang Liu 1,2, Lingling Li 1, Diganta Das 2, Ijaz Haider Naqvi 2,3, and Michael Pecht 2
1 State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Tianjin 300000, China
2 Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, USA
3 Department of Electrical Engineering, Lahore University of Management Sciences (LUMS), Lahore 54792, Pakistan
Insulated-gate bipolar transistors (IGBTs) are one of the most vulnerable components that account for a significant fraction of inverter and converter failures. This paper conducts a degradation analysis of IGBTs using run-to-failure measurements. Online assessment of the degradation state of IGBTs can prolong normal operation and enable proactive maintenance of the system. The research idea is to find a reliable and robust mechanism for IGBT degradation assessment. This paper developed a prediction interval-based degradation assessment methodology that accurately classifies different health states or degradation levels of IGBTs by adding prediction bounds and using them as a critical value for serious damage. It first computes the prediction interval and then uses the Mahalanobis distance to classify the state into degradation level 1 and degradation level 2, instead of just applying the base algorithm. The developed method outperforms distance-based classification schemes and self-organizing maps for online assessment of degradation levels. It only requires training of 1000 initial points which are assumed to be healthy. Furthermore, the generalizability of the method has been shown by validating the effectiveness of the proposed method on three other modules.