2019
DOI: 10.3390/s19112504
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Hybrid Data Fusion DBN for Intelligent Fault Diagnosis of Vehicle Reducers

Abstract: 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 hyb… Show more

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Cited by 20 publications
(12 citation statements)
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“…[18], [29], deep belief neural (DBN)network in Ref. [30] and recurrent neural network(RNN) in Ref. [31] are adopted to diagnose PMSM faults.…”
Section: Comparison With Other Learning Networkmentioning
confidence: 99%
“…[18], [29], deep belief neural (DBN)network in Ref. [30] and recurrent neural network(RNN) in Ref. [31] are adopted to diagnose PMSM faults.…”
Section: Comparison With Other Learning Networkmentioning
confidence: 99%
“…Under the semi-structured environment of the Dutch greenhouse [35], the authors designed a two-wheel differential drive AGV called the BigPan [36]. The architecture of the self-designed AGVfor pesticide spraying is illustrated in Figure. 2 and Table. 1.…”
Section: B Organizationmentioning
confidence: 99%
“…(4) Repeat the "move-search" process according to the planned route until all tasks have been completed or reached a "termination point". (Note: The user can manually set the termination node or when the ITS needs to return to the service station when it detects a failure [55]. )…”
Section: Vision-based Navigation Systemmentioning
confidence: 99%
“…We designed and implemented a complete ITS system based on the above analysis and given the greenhouse application scenario [55]. Figure 10a shows the schematic diagram of a full-featured prototype.…”
Section: An Its Instance For Greenhouse Spraying Applicationmentioning
confidence: 99%
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