在多种操作条件下检测和分类电机轴承的故障
Muhammad Asim Abbasi1, Shiping Huang2, Aadil Sarwar Khan3
1School of Civil Engineering and Transportation, South China University of Technology, 510641, Guangzhou, China; China-Singapore International Joint Research Institute, 510555, Guangzhou, China.
ISA transactions
|November 26, 2024
概括
本研究引入了一种新的多任务方法,用于电机轴承故障检测和分类. 它可以在各种操作条件下准确识别多个故障,提高设备的可靠性.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 状态监控 状态监控
背景情况:
- 电机轴承是关键部件,在苛刻的环境中容易发生故障.
- 有效的状态监控对于防止故障和确保运行安全至关重要.
- 现有的智能故障诊断方法在多条件和多故障场景中面临挑战.
研究的目的:
- 开发和评估用于电机轴承故障检测和分类的多任务框架.
- 解决目前在不同操作条件和多种故障类型中诊断故障的方法的局限性.
主要方法:
- 提出了一种多任务学习方法,用于同时检测和分类故障.
- 使用了HUST电机轴承数据集,包括各种操作条件和多种故障类型.
- 在现实的,复杂的轴承故障场景中评估框架的性能.
主要成果:
- 拟议的多任务框架在故障检测和分类方面表现强.
- 该方法成功地处理了各种操作条件,并发现了多个同时发生的轴承故障.
- HUST 数据集在验证该方法在现实世界的实验故障场景中的有效性方面被证明是有价值的.
结论:
- 多任务故障诊断方法显著提高了机械设备的安全性和可靠性.
- 该方法表现出适应各种旋转机械操作条件的适应性.
- 这项工作有助于推进关键机械元件的智能状态监控.
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