Hierarchical segment-channel attention network for explainable multichannel signal classification

Abstract

Multichannel signal data collected from multiple sensors are widely used to monitor the status of various mechanical systems. Recently, deep neural networks have been success-fully applied to multichannel signal data analysis because of their capability to learn dis-criminative 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 mul-tichannel signals and correctly identifying the critical segments and channels. (c) 2021 Elsevier Inc. All rights reserved. With recent advances in sensor technology regarding hardware and software for data storage and wireless communication, the use of multiple sensors of monitoring of various systems based on multiple sensors has become more prevalent than ever. Signals collected from the sensors can be used to describe states or to detect system malfunctions of a system. Examples include human activity recognition [1], automobile statement recognition [2], and monitoring construction equipment monitoring [3]. Various methods have been considered for analyzing complex multivariate time series data obtained from multiple sen