Data driven prognostics for predicting remaining useful life of IGBT

Power electronic devices such IGBT (Integrated Gate Bipolar Transistor) are used in wide range of applications such as automotive, aerospace and telecommunications. The ability to predict degradation of power electronic components can minimise the risk of their failure while in operation. Research in this area aims to develop prognostics strategies for predicting degradation behaviour, failure modes and mechanisms, and remaining useful life of these electronic components. In this paper, data driven prognostics approaches based on Neural Network (NN) and Adaptive Neuro Fuzzy Inference System (ANFIS) models are developed and used to predict the degradation of an IGBT device. IGBT life data under thermal overstress load condition with square signal gate voltage bias, available from NASA prognostics data repository, is used to demonstrate the proposed data-driven prognostics strategy. The monitored collector-emitter voltage is used to identify the pattern and duration of different phases in the applied voltage load. The NN and ANFIS models are trained with a subset of the test data to predict remaining useful life (RUL) of the IGBT device under varying load test profiles. The predictive capability and performance of the models is observed and analysed

Prognostics and health management of power electronics

Prognostics and health management (PHM) is a major tool enabling systems to evaluate their reliability in real-time operation. Despite ground-breaking advances in most engineering and scientific disciplines during the past decades, reliability engineering has not seen significant breakthroughs or noticeable advances. Therefore, self-awareness of the embedded system is also often required in the sense that the system should be able to assess its own health state and failure records, and those of its main components, and take action appropriately. This thesis presents a radically new prognostics approach to reliable system design that will revolutionise complex power electronic systems with robust prognostics capability enhanced Insulated Gate Bipolar Transistors (IGBT) in applications where reliability is significantly challenging and critical. The IGBT is considered as one of the components that is mainly damaged in converters and experiences a number of failure mechanisms, such as bond wire lift off, die attached solder crack, loose gate control voltage, etc. The resulting effects mentioned are complex. For instance, solder crack growth results in increasing the IGBT’s thermal junction which becomes a source of heat turns to wire bond lift off. As a result, the indication of this failure can be seen often in increasing on-state resistance relating to the voltage drop between on-state collector-emitter. On the other hand, hot carrier injection is increased due to electrical stress. Additionally, IGBTs are components that mainly work under high stress, temperature and power consumptions due to the higher range of load that these devices need to switch. This accelerates the degradation mechanism in the power switches in discrete fashion till reaches failure state which fail after several hundred cycles. To this end, exploiting failure mechanism knowledge of IGBTs and identifying failure parameter indication are background information of developing failure model and prognostics algorithm to calculate remaining useful life (RUL) along with ±10% confidence bounds. A number of various prognostics models have been developed for forecasting time to failure of IGBTs and the performance of the presented estimation models has been evaluated based on two different evaluation metrics. The results show significant improvement in health monitoring capability for power switches.Furthermore, the reliability of the power switch was calculated and conducted to fully describe health state of the converter and reconfigure the control parameter using adaptive algorithm under degradation and load mission limitation. As a result, the life expectancy of devices has been increased. These all allow condition-monitoring facilities to minimise stress levels and predict future failure which greatly reduces the likelihood of power switch failures in the first place

Relibility enhance powertrain using ANFIS base prognostics model

In the past decades power electronics have become more interested devices for underpinning research towards the feasibility of new generation of electrical vehicle (EV) which helping to reduce the reliance on fossil fuels. Power electronic semiconductor devices play an important role in power electronic converter and inverter and rectification systems and design enhance the efficiency of EV performance as well as lowering the cost of electric power propulsion systems. The aim of this paper is to develop a prognostics capability for estimating remaining useful life (RUL) of power electronics components. There is a need for an efficient prognostics algorithm that is embeddable and able to improve on the current prognostic models. A positive aspect of this approach is that the IGBT failure model develops using fuzzy logic adapts prognostic model with the fuzzy nature of failure mechanism. Actually, this method is like adaptive neuro-fuzzy inference system (ANFIS). We also compare the results from the proposed prognostic model with stochastic Monte-Carlo approach which can efficiently estimate the remaining useful life of Insulated Gate Bipolar Transistor (IGBT). The RUL (i.e. mean and confident bounds) is then calculated from the simulated of the estimated degradation states to support on-board real-time decision-making. The prognostics results are evaluated using RMSE prognostics evaluation metrics