ABSTRACT
Complex manufacturing systems produce highly engineered products with long product cycle times and are characterized by complex production process behaviors. Ensuring the reliability of these systems is critical to meet customer demands, improve product quality and minimize production losses. The collection and storage of data by sensors and information systems respectively enable the automatic generation and analysis of reliability models of complex manufacturing systems, reducing the need for expert knowledge of the processes. In this article, we propose a novel approach to generate data-driven reliability models of complex manufacturing systems using stochastic Petri nets as the modeling formalism. Our method extracts models from event logs that capture relevant events related to material flow in a system, and state logs, that capture operational state changes in a system’s production resources using process mining. We, furthermore, simulate the derived data-driven reliability models using discrete-event simulation and validate the models to ensure their robustness. We demonstrate the successful application of our method using a case study from the wafer fabrication domain. The results of our case study indicate that data-driven reliability assessment of complex manufacturing systems is feasible and can provide rapid insights into such systems. In addition, the extracted models can be used to support decisions related to maintenance planning, parts procurement and system configuration.
- Angela Adamyan and David He. 2002. Analysis of sequential failures for assessment of reliability and safety of manufacturing systems. Reliability Engineering & System Safety 76, 3 (June 2002), 227–236. https://doi.org/10.1016/S0951-8320(02)00013-3Google ScholarCross Ref
- Elvio Gilberto Amparore, Gianfranco Balbo, Marco Beccuti, Susanna Donatelli, and Giuliana Franceschinis. 2016. 30 years of GreatSPN. In Principles of Performance and Reliability Modeling and Evaluation. Springer, 227–254.Google Scholar
- Jerry Banks, John Carson, Barry Nelson, and David Nicol. 2010. Discrete-event System Simulation. Prentice Hall.Google Scholar
- M. Bertolini, M. Bevilacqua, and G. Mason. 2006. Reliability design of industrial plants using Petri nets. Journal of Quality in Maintenance Engineering 12, 4 (Jan. 2006), 397–411. https://doi.org/10.1108/13552510610705955Google ScholarCross Ref
- Wallace R. Blischke and D. N. Prabhakar Murthy. 2011. Reliability: Modeling, Prediction, and Optimization. John Wiley & Sons.Google Scholar
- Jonas Friederich, Deena P. Francis, Sanja Lazarova-Molnar, and Nader Mohamed. 2022. A Framework for Data-Driven Digital Twins of Smart Manufacturing Systems. Computers in Industry 136 (April 2022), 103586. https://doi.org/10.1016/j.compind.2021.103586Google ScholarDigital Library
- Jonas Friederich and Sanja Lazarova-Molnar. 2021. Process Mining for Reliability Modeling of Manufacturing Systems with Limited Data Availability. In 2021 8th International Conference on Internet of Things: Systems, Management and Security. IEEE, Gandia, Spain, 1–7. https://doi.org/10.1109/IOTSMS53705.2021.9704921Google Scholar
- Jonas Friederich and Sanja Lazarova-Molnar. 2021. Towards Data-Driven Reliability Modeling for Cyber-Physical Production Systems. Procedia Computer Science 184C (2021), 589–596.Google Scholar
- Jonas Friederich and Sanja Lazarova-Molnar. 2022. Data-Driven Reliability Modeling of Smart Manufacturing Systems Using Process Mining. In 2022 Winter Simulation Conference (WSC). IEEE, 2534–2545. https://doi.org/10.1109/WSC57314.2022.10015301Google Scholar
- Jonas Friederich, Giovanni Lugaresi, Sanja Lazarova-Molnar, and Andrea Matta. 2022. Process Mining for Dynamic Modeling of Smart Manufacturing Systems: Data Requirements. In International Conference on Manufacturing Systems 2022. Elsevier B.V., Lugano, Switzerland, 546–551.Google Scholar
- P.J. Haas. 2004. Stochastic Petri nets for modelling and simulation. In Proceedings of the 2004 Winter Simulation Conference, 2004., Vol. 1. IEEE, 112. https://doi.org/10.1109/WSC.2004.1371307Google ScholarCross Ref
- Edward Y Hua and Sanja Lazarova-Molnar. 2022. Validation of Digital Twins: Challenges and Opportunities. In Proceedings of the 2022 Winter Simulation Conference. IEEE, Singapore, 12.Google ScholarCross Ref
- Sohag Kabir and Yiannis Papadopoulos. 2019. Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review. Safety Science 115 (June 2019), 154–175. https://doi.org/10.1016/j.ssci.2019.02.009Google ScholarCross Ref
- Amit Kumar, Vinod Kumar, Vikas Modgil, Ajay Kumar, and Anita Sharma. 2021. Performance Analysis of Complex Manufacturing System using Petri Nets Modeling Method. Journal of Physics: Conference Series 1950, 1 (Aug. 2021), 012061. https://doi.org/10.1088/1742-6596/1950/1/012061Google ScholarCross Ref
- Sanja Lazarova-Molnar and Nader Mohamed. 2019. Reliability Assessment in the Context of Industry 4.0: Data as a Game Changer. Procedia Computer Science 151 (2019), 691–698. https://linkinghub.elsevier.com/retrieve/pii/S187705091930554XGoogle ScholarDigital Library
- G. Lugaresi and A. Matta. 2020. Generation and Tuning of Discrete Event Simulation Models for Manufacturing Applications. In 2020 Winter Simulation Conference (WSC). IEEE, 2707–2718. https://doi.org/10.1109/WSC48552.2020.9383870Google Scholar
- Giovanni Lugaresi and Andrea Matta. 2021. Automated Manufacturing System Discovery and Digital Twin Generation. Journal of Manufacturing Systems 59 (April 2021), 51–66. https://doi.org/10.1016/j.jmsy.2021.01.005Google ScholarCross Ref
- Michael Milde and Gunther Reinhart. 2019. Automated Model Development and Parametrization of Material Flow Simulations. In 2019 Winter Simulation Conference (WSC). IEEE, 2166–2177. https://doi.org/10.1109/WSC40007.2019.9004702Google Scholar
- In Jae Myung. 2003. Tutorial on Maximum Likelihood Estimation. Journal of Mathematical Psychology 47, 1 (Feb. 2003), 90–100. https://doi.org/10.1016/S0022-2496(02)00028-7Google ScholarDigital Library
- Lars Mönch, John W. Fowler, and Scott J. Mason. 2012. Production Planning and Control for Semiconductor Wafer Fabrication Facilities: Modeling, Analysis, and Systems. Springer Science & Business Media.Google Scholar
- Y. J. Qu, X. G. Ming, Z. W. Liu, X. Y. Zhang, and Z. T. Hou. 2019. Smart Manufacturing Systems: State of the Art and Future Trends. The International Journal of Advanced Manufacturing Technology 103, 9 (Aug. 2019), 3751–3768. https://doi.org/10.1007/s00170-019-03754-7Google ScholarCross Ref
- S.P. Sharma, N. Sukavanam, Naveen Kumar, and Ajay Kumar. 2010. Reliability analysis of complex robotic system using Petri nets and fuzzy lambda‐tau methodology. Engineering Computations 27, 3 (Jan. 2010), 354–364. https://doi.org/10.1108/02644401011029925Google ScholarCross Ref
- Wil van der Aalst. 2016. Data Science in Action. In Process Mining: Data Science in Action, Wil van der Aalst (Ed.). Springer, Berlin, Heidelberg, 3–23. https://doi.org/10.1007/978-3-662-49851-4_1Google Scholar
- V. Volovoi. 2004. Modeling of system reliability Petri nets with aging tokens. Reliability Engineering & System Safety 84, 2 (May 2004), 149–161. https://doi.org/10.1016/j.ress.2003.10.013Google ScholarCross Ref
- Pai Zheng, Honghui wang, Zhiqian Sang, Ray Y. Zhong, Yongkui Liu, Chao Liu, Khamdi Mubarok, Shiqiang Yu, and Xun Xu. 2018. Smart Manufacturing Systems for Industry 4.0: Conceptual Framework, Scenarios, and Future Perspectives. Frontiers of Mechanical Engineering 13, 2 (June 2018), 137–150. https://doi.org/10.1007/s11465-018-0499-5Google ScholarCross Ref
Index Terms
- Equipment-centric Data-driven Reliability Assessment of Complex Manufacturing Systems
Recommendations
Cloud-based manufacturing equipment and big data analytics to enable on-demand manufacturing services
Highlights- Proposed the concept of cloud-based manufacturing equipment and discussed the technical challenges in the context of Industry 4.0.
AbstractMaking manufacturing as on-demand cloud services is a transformative paradigm to achieve the required business flexibility in the context of Industry 4.0 via enabling rapid configuration of loosely-connected manufacturing devices to ...
Graphical abstractDisplay Omitted
A framework for data-driven digital twins of smart manufacturing systems
Highlights- Problems of existing methods for generating simulation models for smart factories are identified.
AbstractAdoption of digital twins in smart factories, that model real statuses of manufacturing systems through simulation with real time actualization, are manifested in the form of increased productivity, as well as reduction in costs and ...
Reliability assessment of multistate flexible manufacturing cells considering equipment failures
Highlights- A more rational method for evaluating the reliability of FMCs is proposed.
- New system-level reliability evaluation metrics of FMCs are defined and calculated.
- Analytical solutions are derived, thereby enabling engineers to ...
AbstractThe reliability evaluation of a flexible manufacturing cell (FMC) is valuable for engineers in comprehending the system's reliability level and making well-informed decisions. Nevertheless, the inherent randomness and heterogeneity in the FMC ...
Comments