M2FN: An end-to-end multi-task and multi-sensor fusion network for intelligent fault diagnosis

Intelligent fault diagnosis based on multi-sensor fusion has gained considerable attention in various modern industrial applications. However, it is still challenging to extract discriminative features from multi-sensor data to provide an accurate and reliable diagnosis. For this purpose, this paper proposes a new multi-task multi-sensor fusion network (M2FN) to improve fault diagnosis performance. The proposed method first uses convolutional neural networks to extract and fuse features from raw vibration and current signals. After that, to improve the discriminative ability of the learned features, a multi-task learning module (MTL) is designed which contains a classification task and a deep metric learning task. Our proposed M2FN model is evaluated on a bearing dataset and a gearbox dataset. Experimental results show that our proposed M2FN method significantly outperforms the compared single-sensor-based and single-task-based methods in terms of diagnosis accuracy, and the learned features present better inter-class discriminability and intra-class concentration through the feature visualization analysis. • An end-to-end M2FN model is proposed with fusion of vibration and current signals. • An MTL module is designed to improve the discriminative ability of the learned features. • Multi-sensor fusion can integrate rich and complementary information for improved accuracy. • Both datasets verify the superiority of the proposed M2FN method.