Phase-type distribution models for performance evaluation of condition-based maintenance
Condition-based maintenance (CBM) is gaining attention due to sensor and cloud-based analytics advancements, but research on its impact on system-level performance is limited. Insufficient understanding during CBM implementation can lead to confidence issues and failures. This study introduces a cla...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Production and Manufacturing Research: An Open Access Journal |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21693277.2024.2380723 |
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| author | Kai-Wen Tien Vittaldas Prabhu |
| author_facet | Kai-Wen Tien Vittaldas Prabhu |
| author_sort | Kai-Wen Tien |
| collection | DOAJ |
| description | Condition-based maintenance (CBM) is gaining attention due to sensor and cloud-based analytics advancements, but research on its impact on system-level performance is limited. Insufficient understanding during CBM implementation can lead to confidence issues and failures. This study introduces a class of models using phase-type distribution to assess three maintenance strategises: run-to-failure (RTF), time-based preventive maintenance (TBM), and CBM. Employing machine health-index, the framework characterizes production performance by estimating effective process times. The model demonstrates how adjusting CBM thresholds influences process time variations and assesses the impact of changing maintenance frequency for TBM. Applied to a smart cellular manufacturing system, the model shows CBM’s early-stage implementation. Findings indicate CBM with optimized thresholds boosts maximum throughput by 6.77%. Further, CBM achieves an additional 6.84% increase assuming corrective maintenance time can be reduced by 20%. This approach can help manufacturing become smarter through smarter maintenance in the Industry 4.0 era and beyond. |
| format | Article |
| id | doaj-art-4363be446a164e85974e53ef0bbf05a5 |
| institution | OA Journals |
| issn | 2169-3277 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Production and Manufacturing Research: An Open Access Journal |
| spelling | doaj-art-4363be446a164e85974e53ef0bbf05a52025-08-20T02:34:04ZengTaylor & Francis GroupProduction and Manufacturing Research: An Open Access Journal2169-32772024-12-0112110.1080/21693277.2024.2380723Phase-type distribution models for performance evaluation of condition-based maintenanceKai-Wen Tien0Vittaldas Prabhu1Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, TaiwanDepartment of Industrial and Manufacturing Engineering, The Pennsylvania State University, State College, PA, USACondition-based maintenance (CBM) is gaining attention due to sensor and cloud-based analytics advancements, but research on its impact on system-level performance is limited. Insufficient understanding during CBM implementation can lead to confidence issues and failures. This study introduces a class of models using phase-type distribution to assess three maintenance strategises: run-to-failure (RTF), time-based preventive maintenance (TBM), and CBM. Employing machine health-index, the framework characterizes production performance by estimating effective process times. The model demonstrates how adjusting CBM thresholds influences process time variations and assesses the impact of changing maintenance frequency for TBM. Applied to a smart cellular manufacturing system, the model shows CBM’s early-stage implementation. Findings indicate CBM with optimized thresholds boosts maximum throughput by 6.77%. Further, CBM achieves an additional 6.84% increase assuming corrective maintenance time can be reduced by 20%. This approach can help manufacturing become smarter through smarter maintenance in the Industry 4.0 era and beyond.https://www.tandfonline.com/doi/10.1080/21693277.2024.2380723Phase-type distributioncondition-based maintenanceeffective process timesmart manufacturingIndustry 4.0 |
| spellingShingle | Kai-Wen Tien Vittaldas Prabhu Phase-type distribution models for performance evaluation of condition-based maintenance Production and Manufacturing Research: An Open Access Journal Phase-type distribution condition-based maintenance effective process time smart manufacturing Industry 4.0 |
| title | Phase-type distribution models for performance evaluation of condition-based maintenance |
| title_full | Phase-type distribution models for performance evaluation of condition-based maintenance |
| title_fullStr | Phase-type distribution models for performance evaluation of condition-based maintenance |
| title_full_unstemmed | Phase-type distribution models for performance evaluation of condition-based maintenance |
| title_short | Phase-type distribution models for performance evaluation of condition-based maintenance |
| title_sort | phase type distribution models for performance evaluation of condition based maintenance |
| topic | Phase-type distribution condition-based maintenance effective process time smart manufacturing Industry 4.0 |
| url | https://www.tandfonline.com/doi/10.1080/21693277.2024.2380723 |
| work_keys_str_mv | AT kaiwentien phasetypedistributionmodelsforperformanceevaluationofconditionbasedmaintenance AT vittaldasprabhu phasetypedistributionmodelsforperformanceevaluationofconditionbasedmaintenance |