Next-generation predictive maintenance: leveraging blockchain and dynamic deep learning in a domain-independent system

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dc.authoridAlabadi, Montdher/0000-0001-7466-6575
dc.authoridHabbal, Adib/0000-0002-3939-2609
dc.contributor.authorAlabadi, Montdher
dc.contributor.authorHabbal, Adib
dc.date.accessioned2024-09-29T16:10:15Z
dc.date.available2024-09-29T16:10:15Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractThe fourth industrial revolution, often referred to as Industry 4.0, has revolutionized the manufacturing sector by integrating emerging technologies such as artificial intelligence (AI), machine and deep learning, Industrial Internet of Things (IIoT), cloud computing, cyber physical systems (CPSs) and cognitive computing, throughout the production life cycle. Predictive maintenance (PdM) emerges as a critical component, utilizing data analytic to track machine health and proactively detect machinery failures. Deep learning (DL), is pivotal in this context, offering superior accuracy in prediction through neural networks' data processing capabilities. However, DL adoption in PdM faces challenges, including continuous model updates and domain dependence. Meanwhile, centralized DL models, prevalent in PdM, pose security risks such as central points of failure and unauthorized access. To address these issues, this study presents an innovative decentralized PdM system integrating DL, blockchain, and decentralized storage based on the InterPlanetary File System (IPFS) for accurately predicting Remaining Useful Lifetime (RUL). DL handles predictive tasks, while blockchain secures data orchestration. Decentralized storage safeguards model metadata and training data for dynamic models. The system features synchronized two DL pipelines for time series data, encompassing prediction and training mechanisms. The detailed material and methods of this research shed light on the system's development and validation processes. Rigorous validation confirms the system's accuracy, performance, and security through an experimental testbed. The results demonstrate the system's dynamic updating and domain independence. Prediction model surpass state-of -the-art models in terms of the root mean squared error (RMSE) score. Blockchain-based scalability performance was tested based on smart contract gas usage, and the analysis shows efficient performance across varying input and output data scales. A comprehensive CIA analysis highlights the system's robust security features, addressing confidentiality, integrity, and availability aspects. The proposed decentralized predictive maintenance (PdM) system, which incorporates deep learning (DL), blockchain technology, and decentralized storage, has the potential to improve predictive accuracy and overcome significant security and scalability obstacles. Consequently, this system holds promising implications for the advancement of predictive maintenance in the context of Industry 4.0.en_US
dc.identifier.doi10.7717/peerj-cs.1712
dc.identifier.issn2376-5992
dc.identifier.pmid38192482en_US
dc.identifier.scopus2-s2.0-85179129237en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.7717/peerj-cs.1712
dc.identifier.urihttps://hdl.handle.net/20.500.14619/8072
dc.identifier.volume9en_US
dc.identifier.wosWOS:001120094800005en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherPeerj Incen_US
dc.relation.ispartofPeerj Computer Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDeep learningen_US
dc.subjectBlockchainen_US
dc.subjectPredictive maintenanceen_US
dc.subjectSmart contracten_US
dc.subjectIIoTen_US
dc.subjectIPFSen_US
dc.titleNext-generation predictive maintenance: leveraging blockchain and dynamic deep learning in a domain-independent systemen_US
dc.typeArticleen_US

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