Reliability-Based Dynamic Maintenance Threshold for Failure Prevention of Continuously Monitored Degrading Systems

Li, Lin; You, Ming‐Yi; Ni, Jun

doi:10.1115/1.3123340

Cited by 33 publications

(31 citation statements)

References 19 publications

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“…Also a case study focusing on a section of an automotive assembly line has been used to illustrate the effectiveness of the proposed method. Li et al [24] calculated a reliability-based dynamic maintenance threshold (DMT) based on the updated equipment status. In this paper the benefits of the DMT are demonstrated in a numerical case study on a drilling process.…”

Section: Literature Reviewmentioning

confidence: 99%

Selecting optimum maintenance strategy by fuzzy interactive linear assignment method

Bashiri

Badri

Hejazi

2011

Applied Mathematical Modelling

110

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a b s t r a c tIn the current competitive environment managers of manufacturing and service organizations try to make their organizations competitive by providing timely delivery of high quality products. Maintenance, as a system, plays a key role in reducing cost, minimizing equipment downtime, improving quality, increasing productivity and providing reliable equipment and as a result achieving organizational goals and objectives. This paper presents a new approach for selecting optimum maintenance strategy using qualitative and quantitative data through interaction with the maintenance experts. This approach has been based on linear assignment method (LAM) with some modifications to develop interactive fuzzy linear assignment method (IFLAM).The proposed approach is an interactive method which uses qualitative and quantitative data to rank the maintenance strategies. This method helps managers to find the best maintenance strategy based on the determined criteria. Maintenance experts also can provide and modify their preference information gradually within the interaction process so as to make the result more reasonable. The proposed method has been illustrated by a numerical example.

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Section: Literature Reviewmentioning

confidence: 99%

Selecting optimum maintenance strategy by fuzzy interactive linear assignment method

Bashiri

Badri

Hejazi

2011

Applied Mathematical Modelling

110

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“…γ i is the coefficient and x i is the influence factor. The negative exponential relationship between the machine degeneration degree and time is also illustrated in [34]. Taking the influence of time (T MA and T ON ) into consideration,…”

Section: A: Reliability Of the Manufacturing Equipmentmentioning

confidence: 95%

A Two-Stage Fault Tolerance Method for Large-Scale Manufacturing Network

Peng

Wang

et al. 2019

IEEE Access

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Nowadays, networked manufacturing paradigm, such as cloud manufacturing, has brought new challenges for fault tolerance methods. Failures and errors of manufacturing services are unavoidable in the large-scale network. Appropriate fault tolerance methods need to be adopted to improve the reliability of the whole manufacturing network. Aiming at solving this problem, a two-stage fault tolerance method is, thus, proposed. In the off-line stage, a LeaderRank-based manufacturing nodes ranking (LNR) algorithm is put forward to rank the manufacturing services according to their significance in fault tolerance. Replication strategies are employed only for critical services to achieve the tradeoff between fault tolerance effect and fault tolerance loss. In the online stage, an A*-based heuristic alternative path searching (AHPS) algorithm is proposed to find suitable replacement schemes for composite services. The experimental results illustrate that the two-stage fault tolerance method can improve the reliability of large-scale manufacturing networks both effectively and efficiently.

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“…Comparing with interval inspection, the research in this area is relatively new but has become increasingly popular. The fundamental difference of CBM with continuous monitoring is that the real-time system information allows maintenance decisions to be made at any time and hence the greater chance to optimize the set criteria [123][124][125][126][127][128]. While majority of the existing work deals only with single component, some researchers extend CBM to maintenance decision-making for multiple components in the system.…”

Section: Condition Based Maintenancementioning

confidence: 99%

Prognostics and Health Management: A Review on Data Driven Approaches

Tsui

Chen

Qiang

et al. 2015

Mathematical Problems in Engineering

287

132

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Prognostics and health management (PHM) is a framework that offers comprehensive yet individualized solutions for managing system health. In recent years, PHM has emerged as an essential approach for achieving competitive advantages in the global market by improving reliability, maintainability, safety, and affordability. Concepts and components in PHM have been developed separately in many areas such as mechanical engineering, electrical engineering, and statistical science, under varied names. In this paper, we provide a concise review of mainstream methods in major aspects of the PHM framework, including the updated research from both statistical science and engineering, with a focus on data-driven approaches. Real world examples have been provided to illustrate the implementation of PHM in practice.

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Reliability-Based Dynamic Maintenance Threshold for Failure Prevention of Continuously Monitored Degrading Systems

Cited by 33 publications

References 19 publications

Selecting optimum maintenance strategy by fuzzy interactive linear assignment method

Selecting optimum maintenance strategy by fuzzy interactive linear assignment method

A Two-Stage Fault Tolerance Method for Large-Scale Manufacturing Network

Prognostics and Health Management: A Review on Data Driven Approaches

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