在电机故障诊断中对人工智能技术的审查
Christos Zachariades1, Vigila Xavier1
1Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 7ZX, UK.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
这篇评论分析了用于旋转电机故障诊断的人工智能. 它评估人工智能方法以提高诊断准确性和系统可靠性,为工程师提供实用指导.
科学领域:
- 电气工程
- 计算机科学
- 人工智能
背景情况:
- 旋转电机是重要的工业资产.
- 这些机器的故障会导致大量停机时间和安全问题.
- 有效的故障诊断对于运行完整性至关重要.
研究的目的:
- 提供对电气机器故障诊断的AI技术的全面审查.
- 根据准确性,适应性和复杂性评估不同的人工智能方法.
- 引导研究人员和工程师开发先进的诊断解决方案.
主要方法:
- 监督,无监督,深度学习和混合/整体AI方法的分类和评估.
- 对每个人工智能方法的优点和局限性的比较分析.
- 讨论像可解释的人工智能和数字双胞胎这样的新兴趋势.
主要成果:
- 人工智能技术为旋转电机的故障诊断提供了多种功能.
- 每个AI类别在诊断性能和实施方面都有独特的权衡.
- 新兴趋势有望提供更强大,更易于解释的诊断系统.
结论:
- 人工智能驱动的故障诊断对于提高旋转电机的可靠性至关重要.
- 提供了一个模块化框架和实用建议.
- 这一审查是开发可扩展和可解释的人工智能诊断解决方案的参考.
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