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Open AccessArticle

Hybrid Data Fusion DBN for Intelligent Fault Diagnosis of Vehicle Reducers

1
School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
2
College of Economics and Management, Hubei Engineering University, Xiaogan 432100, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(11), 2504; https://doi.org/10.3390/s19112504
Received: 23 April 2019 / Revised: 23 May 2019 / Accepted: 27 May 2019 / Published: 31 May 2019
Given its importance, fault diagnosis has attracted considerable attention in the literature, and several machine learning methods have been proposed to discover the characteristics of different aspects in fault diagnosis. In this paper, we propose a Hybrid Deep Belief Network (HDBN) learning model that integrates data in different ways for intelligent fault diagnosis in motor drive systems, such as a vehicle drive system. In particular, we propose three data fusion methods: data union, data join, and data hybrid, based on detailed data fusion research. Additionally, the significance of the fusion is explained from the energy perspective of the signal. In particular, the appropriate fusion methods and data structures suitable for model training requirements can help improve the accuracy of fault diagnosis. Moreover, mixed-precision training is used as a special fusion method to further improve the performance of the model. Experiments with the datasets obtained from the simulation platform demonstrate the superiority of our proposed model over the state-of-the-art methods. View Full-Text
Keywords: fault diagnosis; hybrid data fusion; mixed-precision training; hybrid deep belief nets; vehicle reducer fault diagnosis; hybrid data fusion; mixed-precision training; hybrid deep belief nets; vehicle reducer
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MDPI and ACS Style

Zhang, T.; Li, Z.; Deng, Z.; Hu, B. Hybrid Data Fusion DBN for Intelligent Fault Diagnosis of Vehicle Reducers. Sensors 2019, 19, 2504.

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