Hierarchical segment-channel attention network for explainable multichannel signal classification

Multichannel signal data collected from multiple sensors are widely used to monitor the status of various mechanical systems. Recently, deep neural networks have been successfully applied to multichannel signal data analysis because of their capability to learn discriminative features with minimum feature engineering. However, the latest deep neural networks for multichannel signal analysis lack explainability, which is essential for post hoc analysis in various fields. In this study, we propose an explainable neural network for the multichannel signal classification task. The proposed method is equipped with two levels of attention mechanisms –at the segment and channel levels– encouraging the model to focus on important parts in discriminating the status of a system. The derived attention probabilities facilitate interpretation of network behavior and thus can support post hoc analysis. To demonstrate the practicality and applicability of the proposed method, we conducted experiments on both simulated and real-world automobile data. The results confirmed that the proposed method is capable of accurately classifying multichannel signals and correctly identifying the critical segments and channels.