Remaining Useful Life Prediction Based on Interpretable Serialized Variational Autoencoder: A Drift-Diffusion Stochastic Equation Perspective

As a proactive maintenance approach, remaining useful life (RUL) prediction plays a key role in smart operation and maintenance of industrial systems. To enhance the interpretability of deep neural network, and to measure the uncertainty of complex systems in the degradation process, an RUL prediction approach based on interpretable serialized variational autoencoder with drift-diffusion stochastic equation (ISVAE-DDSE) is proposed. Specifically, considering a dynamic sequential modeling method, this article proposes a generative deep learning approach to ensure that the model effectively captures the distribution characteristics of degradation data. On this basis, from the perspective of probabilistic deep generative network, this article derives a new type of generative loss function with the aid of the Bayesian theory. Furthermore, this article proposes an interpretable latent variable construction pattern based on DDSE, which integrates the dynamic representation of states, and rate of state change. In this sense, the network model can understand, and predict the evolutionary behavior of complex systems over time. Moreover, a Gaussian distribution network is designed to evaluate the RUL prediction’s uncertainty. This article demonstrates the advantages of the ISVAE-DDSE using a NASA aircraft turbofan engine dataset.