A Neural Network-Based Survival Analysis Model Considering Censored Data for Failure Prediction

With the rapid development of sensor and information technology, multi-sensor data related to system degradation processes are now readily available for condition monitoring and remaining useful life (RUL) prediction. However, this process is often complicated by the presence of censored sensor data. In this article, we propose a novel method called Bayesian LSTM-SURV, which integrates Survival Analysis (SA) with Neural Networks (NNs) to model the nonlinear relationship between degradation signals and RUL. This method addresses censored signals and the lack of RUL labels through a novel loss function. Additionally, we employ a Bayesian procedure to transfer information from the training data, enhancing the accuracy of predictions for the test data and maximizing the utilization of existing data. Instead of directly predicting RUL values, we assume that the lifetime follows a Weibull distribution. By modeling the lifetime distribution and survival function, we can calculate and predict RUL values while quantifying the uncertainty of these predictions. The advantageous features of the proposed method are demonstrated through simulation studies and its application to a high-fidelity gas turbine engine dataset.