Spatio-temporal Attention-based Hidden Physics-informed Neural Network for Remaining Useful Life Prediction

TL;DR

This paper introduces a novel spatio-temporal attention-based physics-informed neural network for predicting remaining useful life, enhancing accuracy and interpretability by integrating physics knowledge and attention mechanisms.

Contribution

It proposes a new neural network model combining spatio-temporal attention with physics-informed learning for improved RUL prediction accuracy.

Findings

Outperforms existing methods on benchmark datasets.

Effectively captures degradation features with attention mechanisms.

Achieves higher accuracy under complex conditions.

Abstract

Predicting the Remaining Useful Life (RUL) is essential in Prognostic Health Management (PHM) for industrial systems. Although deep learning approaches have achieved considerable success in predicting RUL, challenges such as low prediction accuracy and interpretability pose significant challenges, hindering their practical implementation. In this work, we introduce a Spatio-temporal Attention-based Hidden Physics-informed Neural Network (STA-HPINN) for RUL prediction, which can utilize the associated physics of the system degradation. The spatio-temporal attention mechanism can extract important features from the input data. With the self-attention mechanism on both the sensor dimension and time step dimension, the proposed model can effectively extract degradation information. The hidden physics-informed neural network is utilized to capture the physics mechanisms that govern the evolution of RUL. With the constraint of physics, the model can achieve higher accuracy and reasonable predictions. The approach is validated on a benchmark dataset, demonstrating exceptional performance when compared to cutting-edge methods, especially in the case of complex conditions.