Fault detection in complex mechatronic systems by a hierarchical graph convolution attention network based on causal paths

Fault detection in mechatronic systems is crucial for their maintainability and safety. However, the systems monitoring variables are often abundant, with intricate connections. It is difficult to characterize their relationships and to extract effective features. In this paper, a hierarchical graph convolution attention network based on causal paths (HGCAN) is developed to improve the performance and interpretability of data-driven fault detection models in complex mechatronic systems. A hybrid causal discovery algorithm is introduced to discover the inherit causality among monitoring variables. The causal paths that sequentially connect the cause-effect variables serve as the reception fields to extract features using multiscale convolution. Different levels of the features are aggregated based on a hierarchical attention mechanism, which assigns adaptive weights considering the varied feature importance. To verify the effectiveness of the proposed method, a dataset of real high-speed train braking systems is considered. Experimental results demonstrate promising performance improvement of the proposed method, and analysis on interpretability indicates its potential to facilitate practical decision-making.