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Enhancing reliability in advanced manufacturing systems: A methodology for the assessment of detection and monitoring techniques
KTH, School of Industrial Engineering and Management (ITM), Production engineering.ORCID iD: 0000-0002-8222-503X
KTH, School of Industrial Engineering and Management (ITM), Production engineering, Process Management and Sustainable Industry. Global Industrial Development, Scania CV AB, Hertig Carls Väg 10, Södertälje, 15138, Sweden.ORCID iD: 0009-0008-9137-7087
KTH, School of Industrial Engineering and Management (ITM), Production engineering.ORCID iD: 0000-0001-9185-4607
2025 (English)In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 79, p. 318-333Article in journal (Refereed) Published
Abstract [en]

Advanced manufacturing systems demand the utilization of technologies, methods and capabilities to improve production efficiency or productivity, while ensuring environmental and societal sustainability. Digitalization emerges as an alternative solution for improving the monitoring capabilities of manufacturing systems and consequently enhance the decision-making process. However, the widespread adoption of digital solutions introduces complexities in measurement reliability, data management, and environmental concerns in terms of e-waste and data storing. Therefore, enhancing monitoring capabilities while minimizing resource consumption is crucial for ensuring system reliability in a sustainable way. This research introduces a methodology for assessing the monitoring condition of manufacturing systems. By integrating functional and dysfunctional analysis, approaches that focus on identifying critical functions and potential failure modes of a system, the proposed methodology provides a comprehensive system perspective and targeted directives for improvement. The effectiveness and versatility of the methodology are demonstrated and discussed through its application to various manufacturing systems at a component, machine, and line level.

Place, publisher, year, edition, pages
Elsevier BV , 2025. Vol. 79, p. 318-333
Keywords [en]
Advanced manufacturing systems, Dysfunctional analysis, Effective monitoring, Failure mode and Symptoms Analysis (FMSA), Functional analysis, Monitoring Priority Number (MPN), System reliability
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:kth:diva-359899DOI: 10.1016/j.jmsy.2025.01.015ISI: 001422913900001Scopus ID: 2-s2.0-85216649473OAI: oai:DiVA.org:kth-359899DiVA, id: diva2:1937209
Note

QC 20250303

Available from: 2025-02-12 Created: 2025-02-12 Last updated: 2025-03-03Bibliographically approved

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Gonzalez, MonicaColl-Araoz, Mariano JoseArchenti, Andreas

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