Prognostic Health Monitoring Method For Thermal Fatigue Failure Of Power Module Solder Joints Using The Grain Boundary Sliding Model

摘要

Prognostic health monitoring technologies, which can evaluate performance degradation, load history, and degree of fatigue, have the potential to improve the effective maintenance, reliability design methods, and availability under improper use conditions of electronics systems. We propose a method to assess the thermal fatigue life and thermal load history under asymmetric cycles of a power electronics module by using finite element method-based Lagrangian neural networks. We apply this method to an insulated-gate bipolar transistor power module with a temperature history monitoring function. It is shown that this method can estimate the inelastic strain cycles and thermal fatigue life distribution of the solder joints from temperature monitoring history. It is also confirmed that probabilistic load assessment of thermal cyclic load distributions can be conducted using the estimated inelastic strain cycles. It is concluded that the proposed method could be effective for assessing thermal load history and thermal fatigue life estimation in prognostic health monitoring.