Reliability Evaluation Methodology of Complex Systems Based on Dynamic Object-Oriented Bayesian Networks

Yuan, Xin; Cai, Baoping; Ma, Yunpeng; Zhang, Junyan; Mulenga, Kabwe; Y, Liu; Chen, Guo-Ming

doi:10.1109/access.2018.2810386

Cited by 16 publications

(8 citation statements)

References 28 publications

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“…(3) Construct representation models for P r i and maintenance tasks in O i ; (4) for each P ij ∈ O i do (5) Perform sequence matching between P r i and P ij ; (6) Calculate item weights; 7Calculate SMC H ij ; (8) if H ij < ε i then (9) C i ⟵ P ij ; (10) end if (11) end for (12) end for (13) Return C i ; ALGORITHM 1: Similarity computation algorithm based on maintenance task representation models.…”

Section: Elicitation Of the Prior Distribution Commonly Used Methodsmentioning

confidence: 99%

“…However, it is almost impossible to obtain enough samples for a maintainability demonstration during operational tests and evaluations because the tests are expensive. Generally, the problem of insufficient samples can be dealt with by using probabilistic and statistical approaches, such as Bayesian techniques [3][4][5][6], bootstrap methods [7,8], and Dempster-Shafer (D-S) evidence theory [9][10][11]. Of these, Bayesian methods have increasingly become the de facto option in reliability and maintainability engineering [12].…”

Section: Introductionmentioning

confidence: 99%

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A Maintenance Task Similarity-Based Prior Elicitation Method for Bayesian Maintainability Demonstration

Jian-ping

2020

Mathematical Problems in Engineering

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Prior distribution elicitation is a challenging problem for a Bayesian inference-based mean time to repair (MTTR) demonstration because if inaccurate prior information is introduced into the prior distribution, the results become unreliable. This paper proposes a novel maintenance task representation model based on the similarity of attributed maintenance items. A novel similarity computation algorithm for maintenance tasks is then formulated on the basis of this model. Optimistic and pessimistic values are ascertained from the time data for similar maintenance tasks to obtain a prior distribution. The main idea is to separate maintenance tasks into distinct items and use attribute sets to extract key features. Each pair of items is then compared to quantify the differences between reference and candidate tasks. Candidate tasks with an acceptable difference from the reference task are taken as prior information sources for constructing the prior distribution. A case study involving a high-frequency (HF) transceiver MTTR Bayesian demonstration shows that the proposed method can effectively obtain maintenance tasks similar to those of information sources for prior distribution elicitation.

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Section: Elicitation Of the Prior Distribution Commonly Used Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Maintenance Task Similarity-Based Prior Elicitation Method for Bayesian Maintainability Demonstration

Jian-ping

2020

Mathematical Problems in Engineering

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“…Complex dynamic systems can be modeled by dynamic OOBNs (DOOBNs), which are an integration of DBNs and OOBNs [19]. A DOOBN-based model was used in the reliability evaluation of a water heater process in a previous work, from implementation to operation [20].…”

Section: A Overview Of Bnsmentioning

confidence: 99%

Application of Bayesian Networks in Reliability Evaluation

Cai

Kong

et al. 2019

IEEE Trans. Ind. Inf.

Self Cite

183

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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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“…7 Lin et al 8 constructed the degradation process model of manufacturing system using piecewise Markov process. Yuan et al 9 analyzed the reliability of complex systems under different states using the dynamic Bayesian network. Khatab et al 10 monitored and evaluated the degradation status of manufacturing systems to ensure preventive maintenance at appropriate intervals.…”

Section: Introductionmentioning

confidence: 99%

Root cause identification approach using decomposition of QFD and extended RPN for product manufacturing reliability degradation

Zhang

Wang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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Reliability is reflected in product during manufacturing. However, due to uncontrollable factors during production, product reliability may degrade substantially after manufacturing. Thus, root cause analysis is important in identifying vulnerable parameters to prevent the product reliability degradation in manufacturing. Therefore, a novel root cause identification approach based on quality function deployment (QFD) and extended risk priority number (RPN) is proposed to prevent the degradation of product manufacturing reliability. First, the connotation of product manufacturing reliability and its degradation mechanism are expounded. Second, the associated tree of the root cause of product manufacturing reliability degradation is established using the waterfall decomposition of QFD. Third, the classic RPN is extended to focus on importance to reliability characteristics, probability, and un-detectability. Furthermore, fuzzy linguistic is adopted and the integrated RPN is calculated to determine the risk of root causes. Therefore, a risk-oriented root cause identification technique of product manufacturing reliability degradation is proposed using RPN. Finally, a root cause identification of an engine component is presented to verify the effectiveness of this method. Results show that the proposed approach can identify the root cause objectively and provide reference for reliability control during production.

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Reliability Evaluation Methodology of Complex Systems Based on Dynamic Object-Oriented Bayesian Networks

Cited by 16 publications

References 28 publications

A Maintenance Task Similarity-Based Prior Elicitation Method for Bayesian Maintainability Demonstration

A Maintenance Task Similarity-Based Prior Elicitation Method for Bayesian Maintainability Demonstration

Application of Bayesian Networks in Reliability Evaluation

Root cause identification approach using decomposition of QFD and extended RPN for product manufacturing reliability degradation

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