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

Probabilistic Monte-Carlo Method for Modelling and Prediction of Electronics Component Life

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

Lateral Acceleration Control Design of a Non-linear Homing Missile

Abstract- The paper presents the lateral acceleration control design of non-linear missile model using a multiobjective evolutionary optimisation method (NSGA-II-like). The controller design for the uncertain plants is carried out by minimising gain and phase margins and tracking performance objectives of the corresponding vertices. Pareto surfaces are used to identify a feasible control structure and analyse its performance tradeoffs. Based on the selected trade-off solution, the interpolated controller, whose poles, zeros and gains are linear continuous functions of Mach number and incidence, are designed for the whole operating envelope. The interpolated controller is now synthesised by minimising the Euclidean distance of multiple operating points ’ objective values. The stability is preserved by additionally overlapping these operating regions. The nonlinear simulation results show that the resulting interpolated controller is indeed a robust tracking controller for all possible perturbations.