Machine prognostics under varying operating conditions based on state-space and neural network modeling

Abstract

Prognostics based on deep learning models usually assume that training and testing data come from the same equipment with similar working conditions. In practice, some new working conditions of test equipment may not be recorded in the training dataset, and thus the learned remaining useful life (RUL) prediction model may not work well. This work is the first to propose a novel prognostic model based on state-space modeling and reinforcement learning to predict the RUL of equipment operating under extrapolated new conditions without corresponding training data. Instead of directly constructing a supervised model that relates monitoring measurements to their RUL, a discrete-time state-space model is built using possible failure histories. The contribution of this paper is twofold. First, on the basis of the state-space model, an interpretable prognostic model that combines Lyapunov constraint and reinforcement learning is proposed to predict the equipment RUL. Second, an adversarial training method based on the idea of H∞ robustness is integrated to reduce the effect of state-space modeling errors. The proposed model aims to reduce the causality between RUL and operating parameters and increase the causality between RUL and unobserved degradation characteristics. Consequently, the proposed model is interpretable and has the potential to predict RUL for the equipment operating under conditions beyond the record. The proposed approach is demonstrated and validated using a simulation study and an experimental dataset.