التفاصيل البيبلوغرافية
| العنوان: |
A security‐enhanced equipment predictive maintenance solution for the ETO manufacturing |
| المؤلفون: |
Cao, Xiangyu, Jing, Zhengjun, Zhao, Xiaorong, Xu, Xiaolong |
| المصدر: |
International Journal of Network Management ; ISSN 1055-7148 1099-1190 |
| بيانات النشر: |
Wiley |
| سنة النشر: |
2024 |
| المجموعة: |
Wiley Online Library (Open Access Articles via Crossref) |
| الوصف: |
Summary With the rapid advancement of intelligent manufacturing, ensuring equipment safety has become a crucial prerequisite for enterprise production. In the engineer‐to‐order (ETO) production mode, characterized by diverse equipment types and frequent adjustments in production lines, equipment maintenance has become increasingly complex. Traditional maintenance plans are no longer adequate to meet the evolving demands of equipment maintenance. This paper proposes a security‐enhanced predictive maintenance scheme specifically designed for ETO‐type production equipment. The scheme utilizes industrial Internet of Things (IIoT) technology to monitor machines and equipment, constructs prediction models using machine learning methods, and reinforces the security of the prediction system through adoption of a decentralized architecture with blockchain distributed storage. In this experiment, six supervised learning models were compared, and it was found that the model based on the random forest algorithm achieved an outstanding accuracy rate of 98.88%. Furthermore, the average total response time for generating predictions within the system is 2.0 s, demonstrating a performance suitable for practical equipment maintenance applications. |
| نوع الوثيقة: |
article in journal/newspaper |
| اللغة: |
English |
| DOI: |
10.1002/nem.2263 |
| الإتاحة: |
https://doi.org/10.1002/nem.2263Test |
| حقوق: |
http://onlinelibrary.wiley.com/termsAndConditions#vorTest |
| رقم الانضمام: |
edsbas.D3DE81A6 |
| قاعدة البيانات: |
BASE |
