A Lightweight Neural Network for End-to-End Bearing Fault Diagnosis in Multi-Sensor Scenarios

Abstract

Deep learning techniques have been widely applied in bearing fault diagnosis. However, their inherent reliance on historical offline training data and the large number of parameters pose considerable challenges in meeting the real-time requirements of online fault diagnosis applications, particularly in Industrial Internet of Things (IIoT) and edge computing environments. To address these challenges, this paper introduces a lightweight temporal feature fusion network
(LTFFNet) for processing multi-sensor signals to enable end-to-end bearing fault diagnosis. Instead of following the prevalent approach of converting one-dimensional vibration signals into two-dimensional images for feature extraction and classification, we designed the architecture directly from the perspective of temporal signals. Besides, the incorporation of the Squeeze-and-Excitation (SE) module allows the network to adaptively recalibrate channel-wise feature responses. We assessed the accuracy and real-time performance of the developed network on an embedded platform using the CWRU bearing dataset. The results demonstrate high diagnostic capability and low computational time, indicating its effectiveness and suitability for real-time multi-sensor bearing fault diagnosis in industrial settings.