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

•Dynamic routing-based multimodal neural network for multi-sensory data fusion.•>Multimodal feature extraction schema is designed to enhance the diagnostic performance.•>Effectiveness is experimentally validated with induction motor fault dataset. Induction motor is the main drive power in mod...

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Bibliographic Details
Published in:Journal of manufacturing systems Vol. 55; pp. 264 - 272
Main Authors: Fu, Peilun, Wang, Jinjiang, Zhang, Xing, Zhang, Laibin, Gao, Robert X.
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.04.2020
Subjects:
data fusion
dynamic routing algorithm
Induction motor fault diagnosis
multimodal deep learning
data fusion
Induction motor fault diagnosis
dynamic routing algorithm
multimodal deep learning
ISSN:0278-6125, 1878-6642
Online Access:Get full text
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Summary:•Dynamic routing-based multimodal neural network for multi-sensory data fusion.•>Multimodal feature extraction schema is designed to enhance the diagnostic performance.•>Effectiveness is experimentally validated with induction motor fault dataset. 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.
ISSN:0278-6125
1878-6642
DOI:10.1016/j.jmsy.2020.04.009