Prediction of residual life of complex electromechanical systems based on small training samples

Abstract

Abstract A model for predicting the residual life of complex electromechanical systems based on small training samples is presented. The developed algorithm involves the use of a probabilistic model of a complex electromechanical system. The statistical characteristics of the model parameters are determined by training samples, that is, by past experience of the functioning of this or similar system. The developed algorithm is based on recognition and forecasting algorithms based on a Wald’s sequential decision-making procedure, a modified V. S. Pugachev canonical decomposition, and an improved Parzen window density estimation algorithm.