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

A Framework for Prognostics and Health Management Applications toward Smart Manufacturing Systems

Prognostics and health management (PHM) has emerged as an intelligent solution to improve the availability of manufacturing systems. PHM consists of system health monitoring, feature extraction, fault diagnosis, and fault prognosis through remaining useful life estimation. However, the application of PHM to manufacturing systems is challenging because systems have become more complex and uncertain. In particular, small and medium-sized enterprises have difficulty in applying PHM due to the lack of internal expertise, time and resources for research and development. The objective of this paper is to develop a framework to provide a readily usable and accessible guideline for PHM application to manufacturing systems. A survey was performed to gather the current practices in dealing with system failures and maintenance strategies in the field. A framework was developed for giving a guideline for PHM application based on common core modules across manufacturing systems and their kinds with respect to the amount of available data and domain knowledge. A reference table was developed to track the PHM techniques for feature extraction, fault diagnosis, and fault prognosis. Finally, fault prognosis of a system was conducted as a case study, following the framework and the reference table to verify its practical use