Dynamic Routing-based Multimodal Neural Network for Multi-sensory Fault Diagnosis of Induction Motor

Abstract Induction motor is the main drive power in modern manufacturing, and timely fault diagnosis of induction motor is of significance to production safety, part quality and maintenance cost control. Data fusion-based diagnosis is attractive for effective utilization of multi-source monitoring information of motors with the development of industrial internet of things. A new multi-sensory fusion model is proposed, named dynamic routing-based multimodal neural network (DRMNN), following the paradigm of multimodal deep learning (MDL). Specifically, the fusion of vibration and stator current signals are investigated. A multimodal feature extraction scheme is designed for dimensionality reduction and invariant features capturing based on multi-source information. Since it is necessary to determine the importance of each modality, a dynamic routing algorithm is introduced in the decision layer to adaptively assign proper weights to different modalities. The effectiveness and robustness of developed DRMNN is demonstrated in the experimental studies performed on a motor test rig. In comparison with similar neural networks without data fusion and other state-of-art fusion techniques, the proposed DRMNN yields better performance.