在油转中的旋转器故障的ResNet诊断
Lei Chen1, Liang Dong1, Zhi-Cai Wu1
1Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang, Jiangsu, 212013, China.
Heliyon
|September 3, 2024
概括
使用剩余网络 (ResNet) 的新诊断框架可以准确检测油中转子故障. 最佳的传感器放置和信号处理显著提高了诊断准确度,提高了操作可靠性.
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:
- 转子不平衡和错位是转油的关键问题,影响运行效率和安全.
- 有效的诊断方法对于早期故障检测和防止灾难性故障至关重要.
研究的目的:
- 开发一种创新的诊断框架,用于识别转子失衡和油转中的错位.
- 通过先进的信号处理和战略性传感器放置来提高诊断效率.
主要方法:
- 一个剩余网络 (ResNet) 模型被用于故障诊断.
- 从八个测量点捕获振动信号,使用故障模拟测试装置进行测试.
- 使用了一维和多维信号处理技术,包括短时间里埃转换 (STFT).
- 传感器的位置在轴承座位的轴向,进口边的垂直方向和出口边的轴向方向上进行了优化.
主要成果:
- 拟议的ResNet框架实现了对旋翼故障的诊断准确度超过98%.
- 最佳的传感器放置显著增加了对旋转器故障的灵敏度.
- 通过STFT处理的时间频率数据产生了最高的诊断准确度.
结论:
- 最佳的信号处理和精确的传感器放置对于精确的转子故障诊断在油转中至关重要.
- 开发的框架提高了能源传输系统的运行可靠性和效率.
- 这项研究为旋转机械的状态监测提供了一个强大的方法.
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