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Artificial Intelligence in Prognostics and Health Management of Engineering Systems

Ochella, Sunday, Shafiee, Mahmood, Dinmohammadi, Fateme (2022) Artificial Intelligence in Prognostics and Health Management of Engineering Systems. Engineering Applications of Artificial Intelligence, 108 . Article Number 104552. ISSN 0952-1976. (doi:10.1016/j.engappai.2021.104552) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:92236)

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https://doi.org/10.1016/j.engappai.2021.104552

Abstract

Prognostics 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.

Item Type: Article
DOI/Identification number: 10.1016/j.engappai.2021.104552
Uncontrolled keywords: Prognostics and health management (PHM); Artificial intelligence (AI); Machine learning (ML); Predictive maintenance; Algorithm; Remaining useful life (RUL); Engineering systems
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA165 Engineering instruments, meters etc. Industrial instrumentation
T Technology > TA Engineering (General). Civil engineering (General) > TA168 Systems engineering
T Technology > TA Engineering (General). Civil engineering (General) > TA401 Materials engineering and construction
T Technology > TJ Mechanical engineering and machinery
T Technology > TJ Mechanical engineering and machinery > Control engineering
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Mahmood Shafiee
Date Deposited: 09 Dec 2021 00:31 UTC
Last Modified: 10 Dec 2021 10:24 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/92236 (The current URI for this page, for reference purposes)
Shafiee, Mahmood: https://orcid.org/0000-0002-6122-5719
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