使用基于声学的机器学习方法进行球轴承故障检测
C B Chandrakala1, Sai Sreevalli Karumanchi1, G P Raghudathesh2
1Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India.
Scientific reports
|December 28, 2025
概括
这项研究引入了一种新的方法,用于使用声振动数据和深度学习来预测球轴承的故障. 该方法在检测轴承故障方面达到99.23%的准确性,改进了现有的机器学习模型.
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:
- 球轴承的故障会导致大量的工业停机时间和成本.
- 使用机器学习 (ML) 和深度学习 (DL) 的预测性维护提供了早期故障检测.
- 声振分析是轴承健康的一个关键指标.
研究的目的:
- 开发一个深度神经网络模型来预测球轴承的故障.
- 使用连续波形变换 (CWT) 来分析振动信号.
- 提高轴承故障诊断的准确性和可靠性.
主要方法:
- 收集了来自故障轴承的声振信号.
- 连续波形转换 (CWT) 用于将信号转换为时间频率计数图.
- 基于LeNet-5架构的卷积神经网络 (CNN) 被训练在这些缩影图上.
- 该模型将轴承状况分为不同的缺陷状态.
主要成果:
- 拟议的基于CWT的深度神经网络实现了99.23%的球轴承故障预测准确度.
- 这种准确性超过了传统的基于数值信号的分类模型.
- 该方法有效地捕获振动信号的时间频率特征,用于故障诊断.
结论:
- 结合CWT和CNN,为实时球轴承故障检测提供了一个高度准确和有效的方法.
- 这种方法代表了工业机械预测性维护的重大进步.
- 该模型比现有的机器学习技术提供了更好的性能,用于轴承故障诊断.
关键词:
声学信号 声学信号 声学信号在美国,CNN是CNN.故障检测 检测故障检测我们的LeNet LeNet随机的森林随机的森林斯卡罗格拉姆是指一个等级图 (scalogram).在XGBoost中使用.更多相关视频
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