Data alignments in machinery remaining useful life prediction using deep adversarial neural networks

Abstract

Recently, intelligent data-driven machinery prognostics and health management have been attracting increasing attention due to the great merits of high accuracy, fast response and easy implementation. While promising prognostic performance has been achieved, the first predicting time for remaining useful life is generally difficult to be determined, and the data distribution discrepancy between different machines is mostly ignored, which leads to deterioration in prognostics. In this paper, a deep learning-based prognostic method is proposed to address the problems. Generative adversarial networks are used to learn the distributions of data in machine healthy states, and a health indicator is proposed to determine the first predicting time. Afterwards, adversarial training is further introduced to achieve data alignments of different machine entities in order to extract generalized prognostic knowledge. Experiments of remaining useful life prediction on two rotating machinery datasets are implemented, and the promising prognostic results validate the effectiveness of the proposed method.