Non-parametric Multi Self-Attention Temporal Convolutional Network for Probabilistic Remaining Useful Life Prediction

Abstract

Massive condition monitoring (CM) data from industrial systems has increased the usability of data-driven methods in prognostics. Remaining useful life (RUL) prediction plays a vital role in helping to improve system reliability and to reduce system risks. However, most of existing data-driven methods for RUL prediction only support point estimation and cannot adaptively extract information from different system features and time periods, but it is important to provide probabilistic RUL prediction results in practice. In this context, we propose a deep learning-based probabilistic RUL prediction framework with multi self-attention mechanisms. It is able to weight CM data in two dimensions and predict the probability density of the target RUL. Specifically, based on the multi self-attention mechanisms, the proposed framework can adaptively extract useful information from both time dimension and feature dimension by weighting measurements from multiple in-suit sensors. Then, a temporal convolution network with the shared weights is applied to feature extraction of the CM data. A non-parametric method is used to obtain a confidence interval (CI) of the target RUL with aleatoric uncertainty. The performance of the proposed framework is evaluated via a public turbofan CM dataset. The results show that the proposed framework can output high-accuracy CI for RUL prediction.