Theoretical Framework for Blockchain Secured Predictive Maintenance Learning Model Using Digital Twin
The automotive sector benefits from Digital Twins (DTs), software replicas of physical assets or processes. DTs enable engineers and data scientists to obtain deeper insights into the system and solve the most difficult problems faster and more affordably. Blockchain technology is a developing and e...
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| Auteurs principaux: | , |
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| Format: | Статья |
| Langue: | English |
| Publié: |
Springer Science and Business Media Deutschland GmbH
2024
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| Accès en ligne: | https://dspace.ncfu.ru/handle/123456789/29301 |
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| Résumé: | The automotive sector benefits from Digital Twins (DTs), software replicas of physical assets or processes. DTs enable engineers and data scientists to obtain deeper insights into the system and solve the most difficult problems faster and more affordably. Blockchain technology is a developing and exciting technology that has the potential to offer DTs monitoring capabilities, strengthening security and enhancing DTs’ transparency, dependability, and immutability. Intelligent behavior can be integrated into blockchain-based DTs to foresee important maintenance tasks and successfully manage machine functions. Our research involves creating a theoretical framework that leverages emerging technologies such as blockchain, artificial intelligence and DTs to facilitate resolution in the predictive maintenance of industry machines with minimised governing cost. |
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