Learning the health index of complex systems using dynamic conditional variational autoencoders

Recent advances in sensing technologies have enabled engineers to collect big data to predict the remaining useful life (RUL) of complex systems. Current modeling techniques for RUL predictions are usually not able to quantify the degradation behavior of a complex system through a health index. Although some studies have been conducted to learn the health index of degradation systems, most of the existing methods are highly dependent on pre-defined assumptions which may not be consistent with the real degradation behaviors. To address this issue, we introduce a time-dependent directed graphical model to characterize the probabilistic relationships among sensor signals, RUL, operational conditions, and health index. Based on the graphical model, a dynamic conditional variational autoencoder is proposed to learn the health index. The experimental results have shown that the proposed method can learn an effective and reliable health index that measures complex system degradation behavior. Moreover, the learned health index improves the accuracy of RUL predictions.