Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 111722 |
| Fachzeitschrift | Reliability Engineering and System Safety |
| Jahrgang | 266 |
| Frühes Online-Datum | 17 Sept. 2025 |
| Publikationsstatus | Veröffentlicht - Feb. 2026 |
Abstract
Accurate reliability assessment of advanced manufacturing systems is essential for ensuring production efficiency, reducing downtime, and enabling intelligent maintenance strategies. In practical industrial environments, state observations obtained from sensors or expert evaluations are often imprecise. Effectively utilizing this uncertain information can substantially improve the precision of reliability evaluations. Conventional methodologies often encounter limitations in addressing this challenge, as manufacturing systems are generally characterized by networked production line configurations rather than traditional serial or parallel structures. Moreover, the effective integration of imprecise observational data is essential for the continuous updating of system reliability. This study introduces a novel approach for dynamic reliability evaluation of a multi-state manufacturing system (MSMS), incorporating both rework mechanisms and buffer elements to enhance the accuracy and applicability of system reliability assessments. The MSMS model can effectively depict the gradual degradation processes and diverse performance levels of manufacturing systems, allowing for a more realistic and detailed representation of system behavior over time compared to traditional binary-state models. This study employs the multistate flow network (MFN) model to construct the MSMS reliability assessment framework from a network structure perspective. Dynamic Bayesian networks (DBNs) are developed to update the reliability function of an individual MSMS by incorporating evidential observational data. An illustrative case study on the reliability update of an aluminum alloy wheel production line is presented to demonstrate the proposed methodology. The case study results further confirm the effectiveness of the approach.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Sicherheit, Risiko, Zuverlässigkeit und Qualität
- Ingenieurwesen (insg.)
- Wirtschaftsingenieurwesen und Fertigungstechnik
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in: Reliability Engineering and System Safety, Jahrgang 266, 111722, 02.2026.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - A dynamic reliability assessment method for multi-state manufacturing system by merging imprecise observational information
AU - Huang, Tudi
AU - Zhang, Qin
AU - Beer, Michael
AU - Liu, Yu
AU - Huang, Hong Zhong
N1 - Publisher Copyright: © 2025 Elsevier Ltd
PY - 2026/2
Y1 - 2026/2
N2 - Accurate reliability assessment of advanced manufacturing systems is essential for ensuring production efficiency, reducing downtime, and enabling intelligent maintenance strategies. In practical industrial environments, state observations obtained from sensors or expert evaluations are often imprecise. Effectively utilizing this uncertain information can substantially improve the precision of reliability evaluations. Conventional methodologies often encounter limitations in addressing this challenge, as manufacturing systems are generally characterized by networked production line configurations rather than traditional serial or parallel structures. Moreover, the effective integration of imprecise observational data is essential for the continuous updating of system reliability. This study introduces a novel approach for dynamic reliability evaluation of a multi-state manufacturing system (MSMS), incorporating both rework mechanisms and buffer elements to enhance the accuracy and applicability of system reliability assessments. The MSMS model can effectively depict the gradual degradation processes and diverse performance levels of manufacturing systems, allowing for a more realistic and detailed representation of system behavior over time compared to traditional binary-state models. This study employs the multistate flow network (MFN) model to construct the MSMS reliability assessment framework from a network structure perspective. Dynamic Bayesian networks (DBNs) are developed to update the reliability function of an individual MSMS by incorporating evidential observational data. An illustrative case study on the reliability update of an aluminum alloy wheel production line is presented to demonstrate the proposed methodology. The case study results further confirm the effectiveness of the approach.
AB - Accurate reliability assessment of advanced manufacturing systems is essential for ensuring production efficiency, reducing downtime, and enabling intelligent maintenance strategies. In practical industrial environments, state observations obtained from sensors or expert evaluations are often imprecise. Effectively utilizing this uncertain information can substantially improve the precision of reliability evaluations. Conventional methodologies often encounter limitations in addressing this challenge, as manufacturing systems are generally characterized by networked production line configurations rather than traditional serial or parallel structures. Moreover, the effective integration of imprecise observational data is essential for the continuous updating of system reliability. This study introduces a novel approach for dynamic reliability evaluation of a multi-state manufacturing system (MSMS), incorporating both rework mechanisms and buffer elements to enhance the accuracy and applicability of system reliability assessments. The MSMS model can effectively depict the gradual degradation processes and diverse performance levels of manufacturing systems, allowing for a more realistic and detailed representation of system behavior over time compared to traditional binary-state models. This study employs the multistate flow network (MFN) model to construct the MSMS reliability assessment framework from a network structure perspective. Dynamic Bayesian networks (DBNs) are developed to update the reliability function of an individual MSMS by incorporating evidential observational data. An illustrative case study on the reliability update of an aluminum alloy wheel production line is presented to demonstrate the proposed methodology. The case study results further confirm the effectiveness of the approach.
KW - Dynamic Bayesian network
KW - Dynamic reliability assessment
KW - Evidence theory
KW - Imprecise reliability data
KW - Multi-state manufacturing system
UR - http://www.scopus.com/inward/record.url?scp=105016532976&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2025.111722
DO - 10.1016/j.ress.2025.111722
M3 - Article
AN - SCOPUS:105016532976
VL - 266
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
SN - 0951-8320
M1 - 111722
ER -