Human-Centric PHM in the Era of Industry 5.0

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Published Jun 27, 2024
Parul Khanna Jaya Kumari Ramin Karim

Abstract

The maintenance industry is undergoing a major transformation as it embraces the shift towards Industry 5.0. The focus of Industry 5.0 is on the integration of human intelligence with advanced technologies. It emphasizes interaction and collaboration between humans and machines and aims to combine the strengths of both. The efficiency of prognostics and health management (PHM) for maintenance in industrial contexts can be enhanced by improving this human-machine interaction and collaboration. This paper investigates the human-centric aspects, with a focus on PHM systems for facilitating the enablement of Industry 5.0 in maintenance. Acknowledging human as an active participant, this study explores their integral role in designing and developing PHM systems. The data collection for this study has been based on available literature, active and passive observations, and unstructured interviews and discussions with experienced industry professionals. As a result of the analysis of collected data, this study identifies and highlights potential areas for research and exploration. The research in these areas can advance the understanding and application of human-centric PHM strategies within Industry 5.0 in maintenance contexts. This is expected to improve the resilience and sustainability aspects of the industrial ecosystem and facilitate the shift towards Industry 5.0.

How to Cite

Khanna, P., Kumari, J., & Karim, R. (2024). Human-Centric PHM in the Era of Industry 5.0. PHM Society European Conference, 8(1), 7. https://doi.org/10.36001/phme.2024.v8i1.4121
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Keywords

Maintenance, Prognostics and Health Management, Industry 5.0, Human-centric

References
Adel, A. (2022). Future of industry 5.0 in society: human centric solutions, challenges and prospective research areas. Journal of Cloud Computing 2022 11:1, 11(1), 1–15. https://doi.org/10.1186/S13677-022-00314-5 Amin, O., Brown, B., Stephen, B., & McArthur, S. (2022). A Case-study Led Investigation of Explainable AI (XAI) to Support Deployment of Prognostics in the industry. PHM Society European Conference, 7(1), 9–20. https://doi.org/10.36001/PHME.2022.V7I1.3336 Beaudreau, B. C. (2018). A Pull–Push Theory of Industrial Revolutions. International Advances in Economic Research, 29(4), 303–317. https://doi.org/10.1007/S11294-023-09883-W Biggio, L., & Kastanis, I. (2020). Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead. Frontiers in Artificial Intelligence, 3, 578613. https://doi.org/10.3389/FRAI.2020.578613/BIBTEX Coleman, D. C. (1956). Industrial Growth and Industrial Revolutions. In New Series (Vol. 23, Issue 89). Ghobakhloo, M., Hannan, ·, Mahdiraji, A., Iranmanesh, M., & Vahid Jafari-Sadeghi, ·. (2024). From Industry 4.0 Digital Manufacturing to Industry 5.0 Digital Society: a Roadmap Toward Human-Centric, Sustainable, and Resilient Production. Information Systems Frontiers 2024, 1–33. https://doi.org/10.1007/S10796-02410476-Z

Ghobakhloo, M., Iranmanesh, M., Tseng, M. L., Grybauskas, A., Stefanini, A., & Amran, A. (2023). Behind the definition of Industry 5.0: a systematic review of technologies, principles, components, and values. Journal of Industrial and Production Engineering , 40(6), 432–447. https://doi.org/10.1080/21681015.2023.2216701 Industry 5.0 - European Commission. (n.d.). Retrieved March 23, 2024, from https://research-andinnovation.ec.europa.eu/research-area/industrialresearch-and-innovation/industry-50_en Industry 5.0: Towards more sustainable, resilient and human-centric industry - European Commission. (n.d.). Retrieved March 24, 2024, from https://research-and-innovation.ec.europa.eu/news/allresearch-and-innovation-news/industry-50-towardsmore-sustainable-resilient-and-human-centricindustry-2021-01-07_en Kamal, A., Nor, M., Rao Pedapati, S., & Muhammad, M.

(n.d.). Explainable AI (XAI) for PHM of Industrial Asset: A State-of-The-Art, PRISMA-Compliant Systematic Review.

Kumar, P., Raouf, I., & Kim, H. S. (2023). Review on prognostics and health management in smart factory: From conventional to deep learning perspectives. Engineering Applications of Artificial Intelligence, 126, 107126. https://doi.org/10.1016/J.ENGAPPAI.2023.107126 Leng, J., Sha, W., Wang, B., Zheng, P., Zhuang, C., Liu, Q., Wuest, T., Mourtzis, D., & Wang, L. (2022). Industry 5.0: Prospect and retrospect. Journal of Manufacturing Systems, 65, 279–295. https://doi.org/10.1016/J.JMSY.2022.09.017 Martinelli, E. M., Farioli, M. C., & Tunisini, A. (2021). New companies’ DNA: the heritage of the past industrial revolutions in digital transformation. Journal of Management and Governance, 25(4), 1079–1106. https://doi.org/10.1007/S10997-020-09539-5 McDonnell, D., Balfe, N., Al-Dahidi, S., & O’Donnell, G. E.

(2014). Designing for Human-Centred Decision Support Systems in PHM. PHM Society European Conference, 2(1). https://doi.org/10.36001/PHME.2014.V2I1.1558 McDonnell, D., Balfe, N., Pratto, L., & O’Donnell, G. E.

(2018). Predicting the unpredictable: Consideration of human and organisational factors in maintenance prognostics. Journal of Loss Prevention in the Process Industries, 54, 131–145. https://doi.org/10.1016/J.JLP.2018.03.008 Moraes, A., Carvalho, A. M., & Sampaio, P. (2023). Lean and Industry 4.0: A Review of the Relationship, Its Limitations, and the Path Ahead with Industry 5.0. Machines, 11(4). https://doi.org/10.3390/MACHINES11040443 Nagano, A. (2019). Thinking about industrial revolutions in systems theory - Moving towards the fourth industrial revolution. ACM International Conference Proceeding Series, Part F148155(2), 470–471. https://doi.org/10.1145/3326365.3326429 Nahavandi, S. (2019). Industry 5.0 —A Human-Centric Solution. Sustainability 2019, Vol. 11, Page 4371 , 11(16), 4371. https://doi.org/10.3390/SU11164371 Nor, A. K. M., Pedapati, S. R., Muhammad, M., & Leiva, V.

(2021). Overview of Explainable Artificial Intelligence for Prognostic and Health Management of Industrial Assets Based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Sensors (Basel, Switzerland), 21(23). https://doi.org/10.3390/S21238020 Poór, P., Ženíšek, D., & Basl, J. (n.d.). Historical Overview of Maintenance Management Strategies: Development from Breakdown Maintenance to Predictive Maintenance in Accordance with Four Industrial Revolutions.

Psarommatis, F., May, G., & Azamfirei, V. (2023).

Envisioning maintenance 5.0: Insights from a systematic literature review of Industry 4.0 and a proposed framework. Journal of Manufacturing Systems, 68, 376–399. https://doi.org/10.1016/J.JMSY.2023.04.009 Raja Santhi, A., & Muthuswamy, P. (2023). Industry 5.0 or industry 4.0S? Introduction to industry 4.0 and a peek into the prospective industry 5.0 technologies. International Journal on Interactive Design and Manufacturing (IJIDeM) 2023 17:2 , 17(2), 947–979. https://doi.org/10.1007/S12008-023-01217-8 Siew, C. Y., Chang, M. M. L., Ong, S. K., & Nee, A. Y. C.

(2020). Human-oriented maintenance and disassembly in sustainable manufacturing. Computers & Industrial Engineering, 150, 106903. https://doi.org/10.1016/J.CIE.2020.106903 Toothman, M., Braun, B., Bury, S. J., Moyne, J., Tilbury, D.

M., Ye, Y., & Barton, K. (2023). Overcoming Challenges Associated with Developing Industrial Prognostics and Health Management Solutions. Sensors 2023, Vol. 23, Page 4009, 23(8), 4009. https://doi.org/10.3390/S23084009 van Oudenhoven, B., Van de Calseyde, P., Basten, R., & Demerouti, E. (2023). Predictive maintenance for industry 5.0: behavioural inquiries from a work system perspective. International Journal of Production Research, 61(22), 7846–7865. https://doi.org/10.1080/00207543.2022.2154403 Verma, A., Bhattacharya, P., Madhani, N., Trivedi, C., Bhushan, B., Tanwar, S., Sharma, G., Bokoro, P. N., & Sharma, R. (2022). Blockchain for Industry 5.0: Vision, Opportunities, Key Enablers, and Future Directions. IEEE Access, 10, 69160–69199. https://doi.org/10.1109/ACCESS.2022.3186892 Zio, E. (2022). Prognostics and Health Management (PHM):Where are we and where do we (need to) go in theory and practice. Reliability Engineering & System Safety,218, https://doi.org/10.1016/J.RESS.2021.108119
Section
Technical Papers