感应电机故障诊断和状态监测方面的进展:全面审查
Kamal Hamani1, Martin Kuchar1, Marek Kubatko1
1Department of Applied Electronics, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu 2172/15, 708 00 Ostrava, Czech Republic.
本综述分析了感应电机 (IM) 故障检测方法,强调了挑战和数据驱动策略的日益增长的作用,如深度学习,以实现高效的电机操作和安全.
科学领域:
- 电气工程 电气工程
- 工业自动化 工业自动化
- 机器状态监测 机器状态监测
背景情况:
- 感应电机 (IM) 是关键的工业部件,容易发生影响效率和安全的故障.
- 及时检测故障对于防止运营停机和严重故障至关重要.
- 现有的故障诊断方法在处理复杂数据特征时面临挑战.
研究的目的:
- 提供当前IM故障检测技术的全面分析.
- 识别现有知识体系中的缺陷和障碍.
- 探索各种故障分类方法在应对数据驱动挑战中的有效性.
主要方法:
- 系统审查IM故障诊断文献.
- 基于IM诊断过程的方法的分类.
- 对数据驱动的挑战 (例如,高维度,类不平衡,非线性,噪音,过度装配) 的故障分类技术的分析.
主要成果:
- 确定了当前IM故障检测研究中的差距和局限性.
- 强调了数据驱动故障诊断策略的日益增长的趋势.
- 证明了深度学习在IM故障诊断中的日益重要和应用.
结论:
- 数据驱动的方法,特别是深度学习,对于克服复杂的故障诊断挑战至关重要.
- 这些领域的进展对现场产生了重大影响,使得智能实时状态监控成为可能.
- 需要进一步的研究,以充分利用智能系统,提高发动机可靠性.
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