Failure prognosis of the components with unlike degradation trends: A data-driven approach

Abstract

Precise remaining useful life (RUL) estimation of components is critical for the prognostic and health management (PHM) of the systems to improve reliability and reduce downtime and maintenance costs. One component may show multiple degradation patterns throughout its life cycle. The degradation trends’ occurrence and recurrence are highly unpredictable. This article suggests an RUL prediction model based on artificial neural network (ANN), for components that show different patterns of degradation while operating under similar working conditions. For the ANN learning, some key time-domain features based on the high correlation of the features with the target output, that is, Life ratio (LR) of the components, are extracted from the history of degradation profiles. Prediction intervals are also estimated to account for the various uncertainties in the degradation profile data. In an application involving accelerated aging of capacitors, when the results of the ANN model are compared to the results of conventional machine learning models for example, Linear Regression, Decision Tree, Support Vector Regression, and Bayesian Neural Network (BNN), it is found that the ANN model gives lowest Mean Square Error (MSE) with limited data, thereby demonstrating the effectiveness of the proposed methodology.