Probabilistic Monte-Carlo Method for Modelling and Prediction of Electronics Component Life
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2014, Vol 5, Issue 1
Abstract
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.
Authors and Affiliations
T. Sreenuch, A. Alghassi, S. Perinpanayagam, Y. Xie
Keywords
Related Articles
- EP ID EP141851
- DOI 10.14569/IJACSA.2014.050113
- Views 97
- Downloads 0
An Investigation and Comparison of Invasive Weed, Flower Pollination and Krill Evolutionary Algorithms
Being inspired by natural phenomena and available biological processes in the nature is one of the difficult methods of problem solving in computer sciences. Evolutionary methods are a set of algorithms that are inspired...
Classifying Natural Language Text as Controlled and Uncontrolled for UML Diagrams
Natural language text fall within the category of Controlled and Uncontrolled Natural Language. In this paper, an algorithm is presented to show that a given language text is controlled or uncontrolled. The parameters an...
Collaborative Editing over Opportunistic Networks: State of the Art and Challenges
Emerging Opportunistic Networks (ON) are under intensive research and development by many academics. However, research efforts on ON only addressed routing protocols as well as data dissemination. Too little attention wa...
Emotion Recognition based on EEG using LSTM Recurrent Neural Network
Emotion is the most important component in daily interaction between people. Nowadays, it is important to make the computers understand user’s emotion who interacts with it in human-computer interaction (HCI) systems. El...
Heterogeneous HW/SW FPGA-Based Embedded System for Database Sequencing Applications
Database sequencing applications including sequence comparison, searching, and analysis are considered among the most computation power and time consumers. Heuristic algorithms suffer from sensitivity while traditional s...