对用于早期轴承故障检测的状态监控算法的评估
Hannes Gruber1, Anna Fuchs1, Michael Bader2
1AVL List GmbH, Hans-List-Platz 1, 8020 Graz, Austria.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
本研究介绍了高级故障指标 (AFI),这是一个用于早期检测滚轮轴承故障的新算法. 国际金融机构使用光谱分析和自适应值来准确识别即将发生的故障,而不会产生错误的阳性.
科学领域:
- 机械工程 机械工程
- 振动分析 振动分析
- 状态监控 状态监控
背景情况:
- 滚筒轴承是机械系统中的关键部件,它们的故障可能导致大量停机时间和成本.
- 早期检测轴承故障对于防止灾难性机械故障和确保操作可靠性至关重要.
研究的目的:
- 开发和验证一个强大的算法,用于早期检测滚子轴承的故障.
- 创建一种方法,尽量减少假阳性故障状态,确保准确的诊断.
- 为高精度轴承和汽车测试台系统量身定制解决方案.
主要方法:
- 利用宽带加速仪振动信号的快速里叶变换 (FFT) 来分析光谱内容.
- 开发了先进的故障指标 (AFI) 算法,用于跟踪频段移动的健康指示.
- 实现了带有移动平均线和波动性分析的自适应值,并通过异常值检测过器进行了增强.
主要成果:
- AFI算法在识别早期轴承故障的微妙偏差方面表现出有效性.
- 对比分析显示,AFI对VRMS,MAVE和EFB等行业标准方法的有效性.
- 该方法成功确认了第一个故障状态,验证了其准确性和可靠性.
结论:
- 提出的AFI算法提供了一种创新的方法,用于早期检测滚筒轴承故障.
- 它的光谱分析,趋势监测,自适应值和异常值检测的结合提高了稳定性和准确性.
- AFI为状态监控提供了可靠的工具,特别是在汽车测试台等苛刻的应用中.
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