A Practical Prognostics Method Based on Stepwise Linear Approximation of a Nonlinear Degradation Model

Abstract

Prognostics aims to predict the remaining useful life (RUL) of an in-service system based on its degradation data. Existing methods, such as artificial neural networks (ANNs) and their variations, often face challenges in real-world applications due to their complexity and the lack of sufficient data. In this paper, a practical prognostic method is proposed, based on the stepwise linear approximation of nonlinear degradation behavior, to simplify the prognostic process while significantly reducing computational costs and maintaining high accuracy. The proposed approach is validated using synthetic data generated at different noise levels, with 100 data sets tested at each level, and compared against a typical ANN method. The results demonstrate that the proposed method consistently outperforms the ANN in terms of accuracy and robustness, while remarkably reducing computational time by a factor of 50 to 60, making it a promising solution for real-world applications.