Reliability assessment of manufacturing systems: A comprehensive overview, challenges and opportunities

被引:35
作者
Friederich, Jonas [1 ]
Lazarova-Molnar, Sanja [1 ,2 ]
机构
[1] Univ Southern Denmark, Maersk Mc Kinney Moller Inst, Campusvej 55, DK-5230 Odense, Denmark
[2] Karlsruhe Inst Technol, Inst AIFB, Kaiserstr 89, D-76133 Karlsruhe, Germany
关键词
Reliability assessment; Manufacturing systems; Literature review; Challenges and opportunities; FAULT-TREE ANALYSIS; MISSION RELIABILITY; INDUSTRY; 4.0; HUMAN ERROR; MAINTENANCE; SIMULATION; FRAMEWORK; CRITIQUE; DECISION; CONTEXT;
D O I
10.1016/j.jmsy.2023.11.001
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Reliability assessment refers to the process of evaluating reliability of components or systems during their lifespan or prior to their implementation. In the manufacturing industry, the reliability of systems is directly linked to production efficiency, product quality, energy consumption, and other crucial performance indicators. Therefore, reliability plays a critical role in every aspect of manufacturing. In this review, we provide a comprehensive overview of the most significant advancements and trends in the assessment of manufacturing system reliability. For this, we also consider the three main facets of reliability analysis of cyber-physical systems, i.e., hardware, software, and human-related reliability. Beyond the overview of literature, we derive challenges and opportunities for reliability assessment of manufacturing systems based on the reviewed literature. Identified challenges encompass aspects like failure data availability and quality, fast-paced technological advancements, and the increasing complexity of manufacturing systems. In turn, the opportunities include the potential for integrating various assessment methods, and leveraging data to automate the assessment process and to increase accuracy of derived reliability models.
引用
收藏
页码:38 / 58
页数:21
相关论文
共 89 条
[1]   Human reliability assessment (HRA) in maintenance of production process: a case study [J].
Aalipour M. ;
Ayele Y.Z. ;
Barabadi A. .
International Journal of System Assurance Engineering and Management, 2016, 7 (02) :229-238
[2]   System failure analysis through counters of Petri net models [J].
Adamyan, A ;
He, D .
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 2004, 20 (04) :317-335
[3]   Analysis of sequential failures for assessment of reliability and safety of manufacturing systems [J].
Adamyan, A ;
He, D .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2002, 76 (03) :227-236
[4]  
Akhavan-Rezai E, 2009, 2009 IEEE BUCHAREST POWERTECH, VOLS 1-5, P968
[5]   On the use of machine learning methods to predict component reliability from data-driven industrial case studies [J].
Alsina, Emanuel F. ;
Chica, Manuel ;
Trawinski, Krzysztof ;
Regattieri, Alberto .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2018, 94 (5-8) :2419-2433
[6]   Industry 4.0: The use of simulation for human reliability assessment [J].
Angelopoulou, Anastasia ;
Mykoniatis, Konstantinos ;
Boyapati, Nithisha Reddy .
INTERNATIONAL CONFERENCE ON INDUSTRY 4.0 AND SMART MANUFACTURING (ISM 2019), 2020, 42 :296-301
[7]  
[Anonymous], 1990, IEEE STD, V610, P64, DOI [DOI 10.1109/IEEESTD.1990.101064, 10.1109/IEEESTD.1990.101064]
[8]   A Methodological Framework for Ontology-Driven Instantiation of Petri Net Manufacturing Process Models [J].
Arena, Damiano ;
Kiritsis, Dimitris .
PRODUCT LIFECYCLE MANAGEMENT AND THE INDUSTRY OF THE FUTURE, 2017, 517 :557-567
[9]  
Bazovsky I., 2004, RELIABILITY THEORY P
[10]  
Birolini A., 1994, QUALITY RELIABILITY