一种新的双输入深度异常检测方法,用于早期故障警告滚动轴承的滚动轴承
Yuxiang Kang1, Guo Chen2, Hao Wang3
1College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
这项研究引入了一种新的双输入深度异常检测方法,用于在滚动轴承中提前警告故障,即使没有故障样本. 该方法在检测异常方面达到98%以上的准确性,优于传统的特征分析.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 滚动轴承是机器中的关键部件,它们的故障可能会导致大量停机时间和成本.
- 传统的故障诊断方法通常需要广泛的标记故障数据,在现实世界中很难获得这种数据.
- 由于缺陷样本不足,故障诊断准确度低,是预测性维护的一个主要挑战.
研究的目的:
- 为滚动轴承开发一个强大的早期故障预警系统.
- 提出一种新的深度异常检测方法,需要零故障样本进行训练.
- 提高滚动轴承故障诊断的准确性和可靠性.
主要方法:
- 一个双输入的深度异常检测框架,利用卷积神经网络 (CNN) 来进行特征提取.
- 集成用于二次特征提取的自动编码器结构和用于改进特征学习的经验库.
- 开发一种新的目标损失函数和波段预处理振动信号,用于多频段频率分析.
- 在正常状态数据上进行无监督学习,用于异常检测.
主要成果:
- 拟议的方法成功地执行了滚动轴承的早期故障预警.
- 实现了超过98%的异常检测准确率.
- 与早期检测中的传统特征 (如RMS和Kurtosis) 相比,在早期检测中表现出卓越的性能.
结论:
- 双输入深度异常检测方法对于滚动轴承的早期故障预警非常有效.
- 零故障样本方法显著克服了故障诊断中的数据稀缺问题.
- 这种方法为提高旋转机械的可靠性和安全性提供了一个有希望的解决方案.
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