使用深度学习框架的轴承机械的异常信息剩余使用寿命估计 (AIRULE)
Pooja Kamat1, Satish Kumar1,2, Shruti Patil1,2
1Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune, Maharashtra, India.
MethodsX
|January 31, 2024
概括
本研究引入了一个深度学习框架,通过检测异常来估计滚动轴承的剩余使用寿命 (RUL). 拟议的方法在预测轴承健康状况方面取得了很高的准确性,提高了机器的安全性并降低了成本.
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:
- 滚动轴承是旋转机械的关键部件,它们的故障会导致大量的生产损失和安全隐患.
- 准确估计剩余使用寿命 (RUL) 对于预测性维护至关重要,确保运营安全和经济效率.
- 目前用于预测的方法需要提高准确性和可靠性.
研究的目的:
- 开发一个智能深度学习框架,准确估计滚动轴承的剩余使用寿命 (RUL).
- 将异常检测集成到 RUL 估计过程中,以提高预测能力.
- 用轴承测试装置的实验数据来验证拟议的框架.
主要方法:
- 实现基于深度学习的框架,使用编码器-解码器长短期内存 (LSTM) 混合模型.
- 从实验测试装置收集轴承状况监测数据,包括振动信号.
- 开发一个异常检测机制,以告知RUL估计.
主要成果:
- 拟议的深度学习框架在剩余使用寿命 (RUL) 估计中表现出高的预测准确性.
- 整合异常检测提高了RUL预测的可靠性.
- 实验验证证证实了拟议方法在现实世界条件下的有效性.
结论:
- 智能深度学习框架为滚动轴承中准确的RUL估计提供了强大的解决方案.
- 这种方法提高了机器的安全性,并通过优化维护计划来节省成本.
- 该研究强调了先进的人工智能技术在条件监测和预测性维护方面的潜力.
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