Immune system inspired smart maintenance framework: tool wear monitoring use case

Pulikottil, Terrin; Martínez-Arellano, Giovanna; Barata, Jose

Abstract

As the manufacturing industry is moving towards the fourth industrial revolution, there is an increasing need for smart maintenance systems that could provide manufacturers with a competitive advantage by predicting failures. Despite various efforts by researchers, there are still challenges for these systems to work reliably in the industry such as lack of adaptability, resilience, reaction to disturbances, and future-proofing. Bio-inspired frameworks like artificial immune systems provide an alternative approach to satisfying these challenges. But existing immune-based frameworks focus only on adaptive immunity characteristics and ignore innate immunity which is important for quick detection and faster response. There is a need for a holistic view of the immune system in developing an adaptive & resilient maintenance framework. This paper presents a holistic view of the human immune system with a focus on the intelligence & response mechanism of both innate & adaptive immunity. Inspired by this holistic view and considering the emerging computer technologies — Internet of Things, Edge & Cloud Computing, Multi-Agent System, Ontology, Big Data, Digital Twin, Machine Learning, and Augmented Reality — we present a smart maintenance framework. The proposed framework is used for tool condition monitoring to demonstrate its implementation.

Citation

Pulikottil, T., Martínez-Arellano, G., & Barata, J. (2024). Immune system inspired smart maintenance framework: tool wear monitoring use case. International Journal of Advanced Manufacturing Technology, 132(9-10), 4699-4721. https://doi.org/10.1007/s00170-024-13472-4

• Journal Article Type: Article
• Acceptance Date: Mar 18, 2024
• Online Publication Date: Apr 24, 2024
• Publication Date: 2024-06
• Deposit Date: Apr 23, 2024
• Publicly Available Date: May 13, 2024
• Journal: The International Journal of Advanced Manufacturing Technology
• Print ISSN: 0268-3768
• Electronic ISSN: 1433-3015
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 132
• Issue: 9-10
• Pages: 4699-4721
• DOI: https://doi.org/10.1007/s00170-024-13472-4
• Keywords: Bio-inspired framework, Machine Learning, Smart maintenance, Artificial immune system, Tool wear monitoring, Predictive maintenance
• Public URL: https://nottingham-repository.worktribe.com/output/34099911
• Publisher URL: https://link.springer.com/article/10.1007/s00170-024-13472-4
• Additional Information: Received: 16 November 2023; Accepted: 18 March 2024; First Online: 24 April 2024; : ; : All authors agree to publish the paper.; : The authors declare no competing interests.