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Updated: Jun 3, 2025

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工业4.0中的故障检测和诊断:对挑战和机遇的回顾
Denis Leite1,2, Emmanuel Andrade2, Diego Rativa2
1Mekatronik I.C. Automacao Ltda, Rua Sargento Silvino Macedo, 130-Imbiribeira, Recife 51160-060, PE, Brazil.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
机器学习 (ML) 通过实时故障检测和诊断 (RT-FDD) 提高了工业可靠性. 在数据质量和整合方面仍然存在挑战,突出显示了对工业实施的研究需求.
科学领域:
- 工业工程 工业工程 工业工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 工业4.0强调机器学习 (ML) 提高生产系统的可靠性和效率.
- 实时故障检测和诊断 (RT-FDD) 对于工业运营至关重要.
- 现有的文献显示了RT-FDD的各种ML方法.
研究的目的:
- 对基于ML的RT-FDD方法进行全面的文献审查.
- 识别该领域的创新方法和持续挑战.
- 突出研究缺口和工业实施的未来方向.
主要方法:
- 对805份文件进行了系统的文献审查.
- 识别和分析29项引人注目的研究,展示基于ML的创新RT-FDD方法.
- 综合了有关效益,挑战和实施差距的调查结果.
主要成果:
- 在805项研究中确定了29项关键研究,重点是基于ML的创新RT-FDD.
- 基于ML的RT-FDD提供了诸如更好的故障预测精度等好处.
- 关键的挑战包括数据质量,模型可解释性和集成复杂性.
结论:
- 在基于ML的RT-FDD的工业实施成果中存在重大差距.
- 未来的研究应该专注于标准化数据集,处理不平衡的数据,并改进特征提取.
- 实施可解释的人工智能 (XAI) 和强调现实世界结果对于推进基于ML的RT-FDD至关重要.
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