一种基于动态异步峰值因子比率的滚动轴承状态监测方法
Guanhua Zhu1, Quansi Huang2, Zeyu Zhang3
1Guangdong Provincial Key Laboratory of Petrochemical Equipment and Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming 525000, China.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
这项研究引入了一种使用Honey Badger算法 (HBA) 来优化动态异步周期的滚动轴承的新型状态监测方法. 该方法提供超过33分钟的预警,用于早期检测滚动轴承的故障.
科学领域:
- 机械工程 机械工程
- 状态监控 状态监控
- 错误诊断 错误诊断 错误诊断 在
背景情况:
- 滚动轴承是机械的关键部件,它们的故障会导致大量停机时间.
- 现有的条件监测方法往往缺乏及时的预警能力,在状态监测方面面临挑战.
- 早期检测滚动轴承故障对于防止灾难性故障和确保运行可靠性至关重要.
研究的目的:
- 开发一个先进的状态监测方法,用于滚动轴承.
- 为了提高早期故障警告的及时性和准确性.
- 解决目前用于检测滚动轴承健康状况的监测技术的局限性.
主要方法:
- 一种基于优化动态异步周期的新型条件监测方法,使用蜂蜜子算法 (HBA).
- 该方法利用峰值因子并比较不同时期的峰值因子,以创建一个动态的异步峰值因子比率监测指数.
- 使用XJTU-SY数据集进行了模拟实验,以验证拟议的方法.
主要成果:
- 与国际标准相比,拟议的方法始终提供超过33分钟的提前预警.
- 对警告数据的信封频谱分析证实了早期故障的存在.
- 开发的监控指标在早期故障检测方面表现出卓越的性能.
结论:
- 新的状态监测方法有效地提高了滚动轴承的早期故障检测.
- 蜂蜜子算法优化显著改善了动态异步周期监控.
- 这种方法为滚动轴承状况监测和预警系统提供了更准确和及时的解决方案.
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