Prognostics of Power MOSFET Using Artificial Neural Network Approach

Abstract

This paper presents the prognostics of in-circuit power MOSFETs in power electronic systems using artificial neural network (ANN). Recent industrial surveys on reliability of power electronic systems shows that the switching transistors are one of most life-limiting component and thermal stress is major cause for their parametric degradation. The on-state drain-source resistance (Rds(on)) is an important fault signature parameter of power MOSFET which increases with its junction temperature rise. In this work, ANN is used to estimate variation in Rds(on) of target MOSFETs at different operating conditions. The data set for training and testing of proposed back-propagation trained artificial neural network are experimentally obtained from the developed test bed. Using test bed, the electrical stressed based accelerated aging test is performed on target power MOSFETs by subjecting them to repetitive unclamped inductive switching at different operating frequency and temperature. After off-line training, the proposed ANN is implemented using National Instruments LabVIEW software to estimate real-time Rds(on) values of in-circuit MOSFETs. For this purpose, voltage fed half bridge inverter circuit is designed and target MOSFETs are taken at output section of the circuit. A low cost microcontroller is programmed for acquiring and serially transmitting the real-time data set of target MOSFETs to the LabVIEW software. The performance of the proposed method is evaluated in real time by comparing the ANN estimated Rds(on) value with the experimental obtained value for in-circuit target MOSFETs at test condition.