Research on Instantiation Method of Object-Oriented Bayesian Network for Fault Diagnosis Based on Dynamic Weight Parameter Transfer

Shigang Zhang (), Mengqiao Chen (), Yongxuan Fang (), Xudong Suo () and Xu Luo ()
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Shigang Zhang: National University of Defense Technology, College of Intelligence Science and Technology
Mengqiao Chen: National University of Defense Technology, College of Intelligence Science and Technology
Yongxuan Fang: National University of Defense Technology, College of Intelligence Science and Technology
Xudong Suo: National University of Defense Technology, College of Intelligence Science and Technology
Xu Luo: National University of Defense Technology, College of Intelligence Science and Technology

A chapter in Data-Driven Methods for Reliability and Safety Engineering: Applications in Industrial Systems, 2026, pp 391-402 from Springer

Abstract: Abstract Bayesian Networks (BNs) are extensively utilized for equipment fault diagnosis, and Object-Oriented Bayesian Networks (OOBNs) offer an effective framework for efficient model construction. In OOBNs, parameter learning, also referred to as instantiation, generally requires an adequate number of training samples. In practical fault diagnosis scenarios, however, such samples are often limited. To address this limitation, an instantiation approach based on variable-weight parameter migration is proposed. When training samples are insufficient, fully instantiated objects from related domains are employed as sources, and their parameters are transferred to enhance instantiation in the target domain. The transfer weight of each source object is dynamically adjusted according to its similarity to the target domain, thereby mitigating the risk of negative transfer. Experimental validation confirms that the proposed method significantly improves OOBN instantiation performance in data-scarce conditions.

Keywords: Fault diagnosis; Instantiation; Object-oriented Bayesian network; Parameter transfer (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-3-032-22873-4_28

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DOI: 10.1007/978-3-032-22873-4_28

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