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- Cai, Baoping, et al.
(författare)
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Application of Bayesian Networks in Reliability Evaluation
- 2019
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Ingår i: IEEE Transactions on Industrial Informatics. - : IEEE. - 1551-3203 .- 1941-0050. ; 15:4, s. 2146-2157
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Tidskriftsartikel (refereegranskat)abstract
- The Bayesian network (BN) is a powerful model for probabilistic knowledge representation and inference and is increasingly used in the field of reliability evaluation. This paper presents a bibliographic review of BNs that have been proposed for reliability evaluation in the last decades. Studies are classified from the perspective of the objects of reliability evaluation, i.e., hardware, structures, software, and humans. For each classification, the construction and validation of a BN-based reliability model are emphasized. The general procedural steps for BN-based reliability evaluation, including BN structure modeling, BN parameter modeling, BN inference, and model verification and validation, are investigated. Current gaps and challenges in reliability evaluation with BNs are explored, and a few upcoming research directions that are of interest to reliability researchers are identified.
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| 2. |
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| 3. |
- Fan, Dongming, et al.
(författare)
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Robustness of maintenance support service networks : attributes, evaluation and improvement
- 2021
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Ingår i: Reliability Engineering & System Safety. - : Elsevier. - 0951-8320 .- 1879-0836. ; 210
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Tidskriftsartikel (refereegranskat)abstract
- Maintenance support service network (MSSN) is used to provide maintenance services and maintain the operational status of equipment. However, the performance of MSSN has been significantly influenced by inevitable disturbance, which makes it vital to maintain its robustness. Existing research on robustness of MSSN mainly focuses on single-layer rather than two-layer network, which imposes constraints on the disturbances and limits its application. To solve these issues, this study develops a two-layer MSSN, consisting of a directed entity-layer and an undirected cyber-layer focusing on supporting maintenance service. A definition of robustness for two-layer MSSN is proposed, and effect propagation models are established to evaluate its robustness of MSSN, followed by its improvement strategies. In particular, two strategies applied in the single-layer MSSN are modified to adapt to the two-layer MSSN, and a novel greedy partnership building approach is proposed to find an optimal strategy under cascading failure, to maintain the robustness of MSSN from a complex network perspective. Finally, numerical examples are presented to illustrate the effectiveness of the proposed approach.
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| 4. |
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| 5. |
- Luo, Jingjie, et al.
(författare)
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Modified DSAN for unsupervised cross-domain fault diagnosis of bearing under speed fluctuation
- 2022
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Ingår i: Journal of manufacturing systems. - : Elsevier B.V.. - 0278-6125 .- 1878-6642. ; 65, s. 180-191
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Tidskriftsartikel (refereegranskat)abstract
- Existing researches about unsupervised cross-domain bearing fault diagnosis mostly consider global alignment of feature distributions in various domains, and focus on relatively ideal diagnosis scenario under the steady speeds. Therefore, unsupervised feature adaptation between all the corresponding subdomains under speed fluctuation remains great challenges. This paper proposes a modified deep subdomain adaptation network (MDSAN) for more practical and challenging cross-domain diagnostic scenarios from the fluctuating speeds to steady speeds. Firstly, to extract the representative features and effectively suppress negative transfer, a novel shared feature extraction module guided by multi-headed self-attention mechanism is constructed. Then, a new trade-off factor is designed to improve the convergence performance and optimization process of MDSAN. The proposed method is used for analyzing experimental bearing vibration data, and the results show that it has higher diagnostic accuracy, faster convergence, better distribution alignment, and is more suitable for unsupervised cross-domain fault diagnosis under speed fluctuation scenario compared with the existing methods.
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