Artificial intelligence in prognostics and health management of engineering systems

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dc.contributor.authorOchella, Sunday
dc.contributor.authorShafiee, Mahmood
dc.contributor.authorDinmohammadi, Fateme
dc.date.accessioned2022-01-24T11:38:27Z
dc.date.available2022-01-24T11:38:27Z
dc.date.issued2021-12-08
dc.description.abstractPrognostics and health management (PHM) has become a crucial aspect of the management of engineering systems and structures, where sensor hardware and decision support tools are deployed to detect anomalies, diagnose faults and predict remaining useful lifetime (RUL). Methodologies for PHM are either model-driven, data-driven or a fusion of both approaches. Data-driven approaches make extensive use of large-scale datasets collected from physical assets to identify underlying failure mechanisms and root causes. In recent years, many data-driven PHM models have been developed to evaluate system’s health conditions using artificial intelligence (AI) and machine learning (ML) algorithms applied to condition monitoring data. The field of AI is fast gaining acceptance in various areas of applications such as robotics, autonomous vehicles and smart devices. With advancements in the use of AI technologies in Industry 4.0, where systems consist of multiple interconnected components in a cyber–physical space, there is increasing pressure on industries to move towards more predictive and proactive maintenance practices. In this paper, a thorough state-of-the-art review of the AI techniques adopted for PHM of engineering systems is conducted. Furthermore, given that the future of inspection and maintenance will be predominantly AI-driven, the paper discusses the soft issues relating to manpower, cyber-security, standards and regulations under such a regime. The review concludes that the current systems and methodologies for maintenance will inevitably become incompatible with future designs and systems; as such, continued research into AI-driven prognostics systems is expedient as it offers the best promise of bridging the potential gap.en_UK
dc.identifier.citationOchella S, Shafiee M, Dinmohammadi F. (2022) Artificial intelligence in prognostics and health management of engineering systems. Engineering Applications of Artificial Intelligence, Volume 108, February 2022, Article number 104552en_UK
dc.identifier.issn0952-1976
dc.identifier.urihttps://doi.org/10.1016/j.engappai.2021.104552
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/17482
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectPrognostics and health management (PHM)en_UK
dc.subjectArtificial intelligence (AI)en_UK
dc.subjectMachine learning (ML)en_UK
dc.subjectPredictive maintenanceen_UK
dc.subjectAlgorithmen_UK
dc.subjectRemaining useful life (RUL)en_UK
dc.subjectEngineering systemsen_UK
dc.titleArtificial intelligence in prognostics and health management of engineering systemsen_UK
dc.typeArticleen_UK

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