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

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

A Discussion on Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms based on Kalman Filter Estimation Applied to Prognostics of Electronics Components

This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process and how it relates to uncertainty representation, management, and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function and the true remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for the two while considering prognostics in making critical decisions

Review of Health Prognostics and Condition Monitoring of Electronic Components

To meet the specifications of low cost, highly reliable electronic devices, fault diagnosis techniques play an essential role. It is vital to find flaws at an early stage in design, components, material, or manufacturing during the initial phase. This review paper attempts to summarize past development and recent advances in the areas about green manufacturing, maintenance, remaining useful life (RUL) prediction, and like. The current state of the art in reliability research for electronic components, mainly includes failure mechanisms, condition monitoring, and residual lifetime evaluation is explored. A critical analysis of reliability studies to identify their relative merits and usefulness of the outcome of these studies' vis-a-vis green manufacturing is presented. The wide array of statistical, empirical, and intelligent tools and techniques used in the literature are then identified and mapped. Finally, the findings are summarized, and the central research gap is highlighted

Breakdown voltage modelling for leatherite paper dielectrics using fuzzy logic technique & estimating the lifetime using step-stress test

OBJECTIVE The real insulation systems are often heterogeneous and some times nonlinear. Quality of insulation is accessed in terms of break down strengths. Partial discharge caused in insulation system by local defects and the resultant overstressing caused by them ultimately lead to breakdown. So the estimation has to be done properly to save insulation from failure. The use of modern computers in bdv analysis has lead to the estimation based on fuzzy logic modeling. The mamdani fuzzy logic using triangular and trapezoidal mf used for the modeling. The bdv got from the modeling section is used to get the weibull parameters using MLE. The shape parameters are used for the life estimation of the dielectric. DESCRIPTION Fuzzy logic modeling is widely used in those fields where the boundary between having a property and not having it is not sharp. The construction of this model can be viewed as a process in which a collection of objects called variables and parameters of the model are related by some other objects called the operators of the model. In the present case it is tried to estimate the bdv of dielectrics depending upon various input conditions. The most important source of partial discharge and breakdown in dielectrics is the voids. Voids are produced due to process control errors at the time of production of most of the solid dielectrics. This is a gas discharged event. The test dielectric is taken as leatherite paper and the estimation is based on data experimentally generated in the laboratory using a CIGRE-2 electrode. The choice of test procedure to know the breakdown voltage of a typical insulation material on insulation system is determined by the test objective. Constant voltage tests provide reliable comprehensive data for the distribution function of the breakdown time but is very time consuming. An accelerated test with increase in voltage stress in discrete steps is quite often used for an electrical insulation study and is widely accepted by the insulation designers. With this method the stress at which the insulation breaks down and time to failure is taken as 6 observed variable The effect of void dimensions on the output is studied and implemented in MATLAB environment. The various steps in modeling include study of the range variation, grouping, rule list generation and simulation. Present system is a MISO system having three inputs (thickness of dielectric, depth and diameter of void) and one output (bdv). The min max algorithm is used as t-norm and s-norm operator. Coa is used for difuzzification. Programming approach is adopted for estimation. The surface plot is plotted to study the variation. Weibull probability has gained wide acceptance in the statistical treatment of time to electrical breakdown of solid dielectrics. It seems to fit experimental data well. MLE is used for parameter estimation. Confidence interval is chosen to get lower and upper limits of the parameters within which the estimation lie for a surety. Throughout the experiment the step stress test is considered. The inverse power law is applied to life estimation. From the slope of the graph the slope is to be found out and used for estimating the life. RESULTS In the mamdani fuzzy logic modeling using the triangular and trapezoidal mf the Mae is found out to be 1.4% and 1.324% respectively. The weibull parameters and life estimation values have close resemblance with the experimentally generated value. CONCLUSION Fuzzy logic provides an easier and a better computation technique based on the fuzzyness of rules. By accurately choosing the parameters and deciding the rule bases the error can be significantly reduced. The weibull parameter calculation using MLE and lifetime also found to be in good agreement. Thus the results indicates that the modeling can be well implemented for such kind of estimation

The safety case and the lessons learned for the reliability and maintainability case

This paper examine the safety case and the lessons learned for the reliability and maintainability case

Cost-efficient modeling of antenna structures using Gradient Enhanced Kriging

Reliable yet fast surrogate models are indispensable in the design of contemporary antenna structures. Data-driven models, e.g., based on Gaussian Processes or support-vector regression, offer sufficient flexibility and speed, however, their setup cost is large and grows very quickly with the dimensionality of the design space. In this paper, we propose cost-efficient modeling of antenna structures using Gradient-Enhanced Kriging. In our approach, the training data set contains, apart from the EM-simulation responses of the structure at hand, also derivative data at the respective training locations obtained at little extra cost using adjoint sensitivity techniques. We demonstrate that introduction of the derivative information into the model allows for considerable reduction of the model setup cost (in terms of the number of training points required) without compromising its predictive power. The Gradient-Enhanced Kriging technique is illustrated using a dielectric resonator antenna structure. Comparison with conventional Kriging interpolation is also provided

Application of Multi-environmental time similarity theory based on relative information (RI-METS) theory in durability of concrete structures in marine chloride environment

The Multi-environmental time similarity (METS) method is a testing method that establishes the similarity relationship between the indoor test environment and the on-site environment to evaluate the durability and predict service life of the proposed or under-constructed concrete structure. Based on the METS theory, a similarity ratio of chloride ion concentration and diffusion coefficient between the indoor accelerated environment and the on-site natural environment was established. Then the relative information entropy was introduced into the Multi-Environmental Time Similarity based on Relative Information (RI-METS) theory to consider the time variability of the diffusion coefficient and the surface chloride ion mass fraction. Then the service life of a component in a marine chloride environment by Monte-Carlo simulation method was predicted