Dual Attention-Based Temporal Convolutional Network for Fault Prognosis Under Time-Varying Operating Conditions

Abstract

Fault prognosis under time-varying operating conditions is critical to predictive maintenance of complex industrial machines. However, prognostic methods proposed in the literature toward varying operating conditions typically focus on limited discrete states with clear boundaries, which are not always encountered in practical scenarios. This article aims to propose a more general method that can handle complex operating conditions with more nonlinearity and dynamics. In this work, a novel deep model named dual attention-based temporal convolutional network (DATCN), is developed to integrate multisensor data with time-varying operating settings for health assessment of industrial machines. An efficient deep temporal convolutional network is introduced to extract degradation features for sequence modeling. A novel dual attention model is developed to adaptively capture the temporal dynamics of sensor data and operating data, further enhancing the feature representations and highlighting the degradation information under time-varying operating settings. Besides, domain adaptation layer with multikernel maximum mean discrepancies (MK-MMD) is embedded to mitigate the domain-shift issue caused by varying operating conditions. The experimental results on the popular turbofan engine data set and a real-world Ion mill etching process validate the effectiveness of the proposed method and show its superiority in yielding significant improvements of prognostic accuracy relative to existing state-of-the-art methods.