基于物联网的数据驱动的预测性维护依赖于模糊系统和人工神经网络
Ashraf Aboshosha1, Ayman Haggag2, Neseem George3,2
1Rad. Eng. Dept., NCRRT, Egyptian Atomic Energy Authority (EAEA), Cairo, Egypt. ashraf.aboshosha@eaea.org.eg.
Scientific reports
|July 27, 2023
概括
本研究介绍了一种数据驱动的预测性维护框架,使用人工智能,物联网和传感器信息建模来增强工业机器维护. 该方法最大限度地减少了故障识别中的人为错误,提高了生产线效率.
科学领域:
- 工业工程 工业工程 工业工程
- 人工智能的人工智能
- 制造系统制造系统的制造
背景情况:
- 工业4.0需要超越反应性和预防性维护 (PM) 的先进维护策略.
- 当前的维护管理面临着效率和基于人为的故障识别错误的挑战.
- 集成先进的自动化和人工智能对于优化工业生产线至关重要.
研究的目的:
- 为工业生产线开发和验证数据驱动的预测性维护 (PdM) 规划框架.
- 利用人工智能,物联网和传感器信息建模 (SIM) 来改进维护管理.
- 在故障识别中最大限度地减少人为错误,提高整体生产效率.
主要方法:
- 实施一个使用AI,物联网和SIM的预测性维护 (PdM) 框架.
- 深度学习 (DL) 的应用用于报警和故障诊断.
- 利用模糊逻辑系统 (FLS) 进行基于AI的预防性维护 (PM).
- 在瓦纸板生产工厂进行实践验证.
主要成果:
- 在现实工业环境中证明了拟议的数据驱动预测维护框架的可行性.
- 通过使用深度学习 (DL) 成功将令人担忧的模式解释为特定的故障.
- 通过SIM和物联网集成,提高工业生产机器维护管理的效率.
结论:
- 拟议的框架为工业4.0环境提供了卓越的维护策略.
- 人工智能驱动的预测性维护显著减少了基于人类的故障识别错误.
- 通过SIM和物联网的整合,提高了工业维护的效率和可靠性.
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