Probabilistic Monte-Carlo method for modelling and prediction of electronics component life

Power electronics are widely used in electric vehicles, railway locomotive and new generation aircrafts. Reliability of these components directly affect the reliability and performance of these vehicular platforms. In recent years, several research work about reliability, failure mode and aging analysis have been extensively carried out. There is a need for an efficient algorithm able to predict the life of power electronics component. In this paper, a probabilistic Monte-Carlo framework is developed and applied to predict remaining useful life of a component. Probability distributions are used to model the component’s degradation process. The modelling parameters are learned using Maximum Likelihood Estimation. The prognostic is carried out by the mean of simulation in this paper. Monte-Carlo simulation is used to propagate multiple possible degradation paths based on the current health state of the component. The remaining useful life and confident bounds are calculated by estimating mean, median and percentile descriptive statistics of the simulated degradation paths. Results from different probabilistic models are compared and their prognostic performances are evaluated

Stochastic RUL calculation enhanced with TDNN-based IGBT failure modeling

Power electronics are widely used in the transport and energy sectors. Hence, the reliability of these power electronic components is critical to reducing the maintenance cost of these assets. It is vital that the health of these components is monitored for increasing the safety and availability of a system. The aim of this paper is to develop a prognostic technique for estimating the remaining useful life (RUL) of power electronic components. There is a need for an efficient prognostic algorithm that is embeddable and able to support on-board real-time decision-making. A time delay neural network (TDNN) is used in the development of failure modes for an insulated gate bipolar transistor (IGBT). Initially, the time delay neural network is constructed from training IGBTs' ageing samples. A stochastic process is performed for the estimation results to compute the probability of the health state during the degradation process. The proposed TDNN fusion with a statistical approach benefits the probability distribution function by improving the accuracy of the results of the TDDN in RUL prediction. The RUL (i.e., mean and confidence bounds) is then calculated from the simulation of the estimated degradation states. The prognostic results are evaluated using root mean square error (RMSE) and relative accuracy (RA) prognostic evaluation metrics

Health Condition Assessment of Multi-Chip IGBT Module with Magnetic Flux Density

To achieve efficient conversion and flexible control of electronic energy, insulated gate bipolar transistor (IGBT) power modules as the dominant power semiconductor devices are increasingly applied in many areas such as electric drives, hybrid electric vehicles, railways, and renewable energy systems. It is known that IGBTs are the most vulnerable components in power converter systems. To achieve high power density and high current capability, several IGBT chips are connected in parallel as a multi-chip IGBT module, which makes the power modules less reliable due to a more complex structure. The lowered reliability of IGBT modules will not only cause safety problems but also increase operation costs due to the failure of IGBT modules. Therefore, the reliability of IGBTs is important for the overall system, especially in high power applications. To improve the reliability of IGBT modules, this thesis proposes a new health state assessment model with a more sensitive precursor parameter for multi-chip IGBT module that allows for condition-based maintenance and replacement prior to complete failure. Accurate health condition monitoring depends on the knowledge of failure mechanism and the selection of highly sensitive failure precursor. IGBT modules normally wear out and fail due to thermal cycling and operating environment. To enhance the understanding of the failure mechanism and the external characteristic performance of multi-chip IGBT modules, an electro-thermal finite element model (FEM) of a multi-chip IGBT module used in wind turbine converter systems was established with considerations for temperature dependence of material property, the thermal coupling effect between components, and the heat transfer process. The electro-thermal FEM accurately performed temperature distribution and the distribution electrical characteristic parameters during chip solder degradation. This study found an increased junction temperature, large change of temperature distribution, and more serious imbalanced current sharing during a single chip solder aging, thereby accelerating the aging of the whole IGBT module. According to the change of thermal and electrical parameters with chip solder fatigue, the sensitivity of fatigue sensitive parameters (FSPs) was analyzed. The collector current of the aging chip showed the highest sensitivity with the chip solder degradation compared with the junction temperature, case temperature, and collector-emitter voltage. However, the current distribution of internal components remains inaccessible through direct measurements or visual inspection due to the package. As the relationship between the current and magnetic field has been studied and gradually applied in sensor technologies, magnetic flux density was proposed instead of collector current as a new precursor for health condition monitoring. Magnetic flux density distribution was extracted by an electro-thermal-magnetic FEM of the multi-chip IGBT module based on electromagnetic theory. Simulation results showed that magnetic flux density had even higher sensitivity than collector current with chip solder degradation. In addition, the magnetic flux density was only related with the current and was not influenced by temperature, which suggested good selectivity. Therefore, the magnetic flux density was selected as the precursor due to its better sensitivity, selectivity, and generality. Finally, a health state assessment model based on backpropagation neural network (BPNN) was established according to the selected precursor. To localize and evaluate chip solder degradation, the health state of the IGBT module was determined by the magnetic flux density for each chip and the corresponding operating conduction current. BPNN featured good self-learning, self-adapting, robustness and generalization ability to deal with the nonlinear relationship between the four inputs and health state. Experimental results showed that the proposed model was accurate and effective. The health status of the IGBT modules was effectively recognized with an overall recognition rate of 99.8%. Therefore, the health state assessment model built in this thesis can accurately evaluate current health state of the IGBT module and support condition-based maintenance of the IGBT module

Two decades of condition monitoring methods for power devices

Condition monitoring (CM) of power semiconductor devices enhances converter reliability and customer service. Many studies have investigated the semiconductor devices failure modes, the sensor technologies, and the signal processing techniques to optimize the CM. Furthermore, the improvement of power devices’ CM thanks to the use of the Internet of Things and artificial intelligence technologies is rising in smart grids, transportation electrification, and so on. These technologies will be widespread in the future, where more and more smart techniques and smart sensors will enable a better estimation of the state of the health (SOH) of the devices. Considering the increasing use of power converters, CM is essential as the analysis of the data obtained from multiple sensors enables the prediction of the SOH, which, in turn, enables to properly schedule the maintenance, i.e., accounting for the trade-off between the maintenance cost and the cost and issues due to the device failure. From this perspective, this review paper summarizes past developments and recent advances of the various methods with the aim of describing the current state-of-the-art in CM research

Reliability Assessment of IGBT through Modelling and Experimental Testing

Lifetime of power electronic devices, in particular those used for wind turbines, is short due to the generation of thermal stresses in their switching device e.g., IGBT particularly in the case of high switching frequency. This causes premature failure of the device leading to an unreliable performance in operation. Hence, appropriate thermal assessment and implementation of associated mitigation procedure are required to put in place in order to improve the reliability of the switching device. This paper presents two case studies to demonstrate the reliability assessment of IGBT. First, a new driving strategy for operating IGBT based power inverter module is proposed to mitigate wire-bond thermal stresses. The thermal stress is characterised using finite element modelling and validated by inverter operated under different wind speeds. High-speed thermal imaging camera and dSPACE system are used for real time measurements. Reliability of switching devices is determined based on thermoelectric (electrical and/or mechanical) stresses during operations and lifetime estimation. Second, machine learning based data-driven prognostic models are developed for predicting degradation behaviour of IGBT and determining remaining useful life using degradation raw data collected from accelerated aging tests under thermal overstress condition. The durations of various phases with increasing collector-emitter voltage are determined over the device lifetime. A data set of phase durations from several IGBTs is trained to develop Neural Network (NN) and Adaptive Neuro Fuzzy Inference System (ANFIS) models, which is used to predict remaining useful life (RUL) of IGBT. Results obtained from the presented case studies would pave the path for improving the reliability of IGBTs

Reliability-Oriented Strategies for Multichip Module Based Mission Critical Industry Applications

The availability is defined as the portion of time the system remains operational to serve its purpose. In mission critical applications (MCA), the availability of power converters are determinant to ensure continue productivity and avoid financial losses. Multichip Modules (MCM) are widely adopted in such applications due to the high power density and reduced price; however, the high number of dies inside a compact package results in critical thermal deviations among them. Moreover, uneven power flow, inhomogeneous cooling and accumulated degradation, potentially result in thermal deviation among modules, thereby increasing the temperature differences and resulting in extra temperature in specific subset of devices. High temperatures influences multiple failure mechanisms in power modules, especially in highly dynamic load profiles. Therefore, the higher failure probability of the hottest dies drastically reduces the reliability of mission critical power converters. Therefore, this work investigate reliability-oriented solutions for the design and thermal management of MCM-based power converters applied in mission critical applications. The first contribution, is the integration of a die-level thermal and probabilistic analysis on the design for reliability (DFR) procedure, whereby the temperature and failure probability of each die are taken into account during the reliability modeling. It is demonstrated that the dielevel analysis can obtain more realistic system-level reliability of MCM-based power converters. Thereafter, three novel die-level thermal balancing strategies, based on a modified MCM - with more gate-emitter connections - are proposed and investigated. It is proven that the temperatures inside the MCM can be overcame, and the maximum temperate reduced in up to 8 %

Challenges and New Trends in Power Electronic Devices Reliability

The rapid increase in new power electronic devices and converters for electric transportation and smart grid technologies requires a deepanalysis of their component performances, considering all of the different environmental scenarios, overload conditions, and high stressoperations. Therefore, evaluation of the reliability and availability of these devices becomes fundamental both from technical and economicalpoints of view. The rapid evolution of technologies and the high reliability level offered by these components have shown that estimating reliability through the traditional approaches is difficult, as historical failure data and/or past observed scenarios demonstrate. With the aim topropose new approaches for the evaluation of reliability, in this book, eleven innovative contributions are collected, all focusedon the reliability assessment of power electronic devices and related components

Application of Kalman filter to estimate junction temperature in IGBT power modules

Knowledge of instantaneous junction temperature is essential for effective health management of power converters, enabling safe operation of the power semiconductors under all operating conditions. Methods based on fixed thermal models are typically unable to compensate for degradation of the thermal path resulting from aging and the effect of variable cooling conditions. Thermosensitive electrical parameters (TSEPs), on the other hand, can give an estimate of junction temperature TJ, but measurement inaccuracies and the masking effect of varying operating conditions can corrupt the estimate. This paper presents a robust and noninvasive real-time estimate of junction temperature that can provide enhanced accuracy under all operating and cooling conditions when compared to model-based or TSEP-based methods alone. The proposed method uses a Kalman filter to fuse the advantages of model-based estimates and an online measurement of TSEPs. Junction temperature measurements are obtained from an online measurement of the on-state voltage, VCE(ON) , at high current and processed by a Kalman filter, which implements a predict-correct mechanism to generate an adaptive estimate of TJ. It is shown that the residual signal from the Kalman filter may be used to detect changes in thermal model parameters, thus allowing the assessment of thermal path degradation. The algorithm is implemented on a full-bridge inverter and the results verified with an IR camer

A State Evaluation Method for Solder Layer in MOSFET

MOSFET is the core component in power equipment. It is widely used in electrical vehicles (EV), wind generation, rail transit and so on. The long-term impact of temperature and stress cause fatigue in the device during operation. Because of the low melting point of 96.5Sn3.5Ag, solder layer aging and failure is one of the main failure modes. So, it is important to figure out the failure mechanism and the effects of defects in the solder layer. A finite element (FE) model considered the temperature dependence of materials was built in COMSOL software to support the subsequent studies. Effects of voids in solder layer and fatigue are studied and analyzed based on the FE model. The results show the junction temperature, case temperature, on-resistance and thermal resistance between junction and case increase with the rise of voids’ areas and fatigue degree. Besides that, all of them have a similar trend, which means on-resistance can be a criterion for thorough failure replacing the thermal resistance. And the on-resistance is more sensitive than thermal resistance because its growth rate is much higher than that of thermal resistance. Based on the simulation and analyzed, on-resistance, case temperature and on-current were selected as the characteristic parameter to reflect the healthy state of MOSFET. They were used as the inputs for the evaluation model. And the growth rate of on-resistance was chosen as the output parameter. Combine the failure rate curve, the range from health to thorough failure was be divided into five pieces with different intervals. For evaluation, adaptive neuro-fuzzy inference system (ANFIS) was adopted to establish the model. By validation and comparing with some common classification algorithms, it was verified and showed high accuracy

In situ diagnostics and prognostics of solder fatigue in IGBT modules for electric vehicle drives

This paper proposes an in situ diagnostic and prognostic (D&P) technology to monitor the health condition of insulated gate bipolar transistors (IGBTs) used in EVs with a focus on the IGBTs' solder layer fatigue. IGBTs' thermal impedance and the junction temperature can be used as health indicators for through-life condition monitoring (CM) where the terminal characteristics are measured and the devices' internal temperature-sensitive parameters are employed as temperature sensors to estimate the junction temperature. An auxiliary power supply unit, which can be converted from the battery's 12-V dc supply, provides power to the in situ test circuits and CM data can be stored in the on-board data-logger for further offline analysis. The proposed method is experimentally validated on the developed test circuitry and also compared with finite-element thermoelectrical simulation. The test results from thermal cycling are also compared with acoustic microscope and thermal images. The developed circuitry is proved to be effective to detect solder fatigue while each IGBT in the converter can be examined sequentially during red-light stopping or services. The D&P circuitry can utilize existing on-board hardware and be embedded in the IGBT's gate drive unit