合作混合建模和尺寸缩小,用于工业系统中的故障监测应用
Morgane Suhas1,2,3, Emmanuelle Abisset-Chavanne1,2, Pierre-André Rey1,2
1Univ. Bordeaux, CNRS, Bordeaux INP, I2M, UMR 5295, F-33400 Talence, France.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
本研究介绍了用于工业直流电机故障监测的合作混合型分类模型 (CHMC). 该CHMC模型增强了故障检测,并适应新的使用配置文件,改进了预测性维护.
科学领域:
- 工程 工程师 工程师 工程师
- 机器学习 机器学习
- 工业系统 工业系统
背景情况:
- 工业系统的可靠性对竞争力至关重要.
- 有效的故障监控需要先进的诊断技术.
- 现有的方法可能会在新的操作条件下扎.
研究的目的:
- 为DC电机故障监测提出一种创新的混合建模方法.
- 为了提高故障检测准确度和新使用配置文件的推断能力.
- 开发一个具有成本效益的解决方案,用于工业环境中的电机故障分类.
主要方法:
- 开发一个合作混合分类模型 (CHMC),将基于物理和数据驱动的模型结合起来.
- 将物理知识集成到循环神经网络中,用于轴承故障检测.
- 单数值分解 (SVD) 的应用,用于特征提取和维度减小.
主要成果:
- 该CHMC模型准确地识别了三个健康状态:健康,渐进性故障和稳定故障.
- 单值分解 (SVD) 减少了90%的输入数据,同时保留了关键信息.
- 与NHMC模型相比,CHMC模型在真负检测和适应新使用配置文件方面表现出优异的性能.
结论:
- 该CHMC模型提供了一个可通用的方法,用于增强故障检测和模型适应.
- 将物理模型与减小维度相结合,可以提高概括性和适应性.
- 该CHMC模型为工业发动机系统的预测性维护提供了具有成本效益的解决方案.
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