基于卷积自编码器的故障诊断方法,用于考虑未知的故障的液压电磁
Seungjin Yoo1, Joon Ha Jung2, Jai-Kyung Lee1
1Department of Smart Industrial Machine Technologies, Korea Institute of Machinery and Materials, Daejeon 34103, Republic of Korea.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
这项研究引入了一种新的诊断技术,用于检测液压电磁故障. 数据驱动模型准确地识别了门故障,提高了系统的可靠性和安全性.
科学领域:
- 机械工程 机械工程
- 电气工程 电气工程
- 数据科学数据科学数据科学
背景情况:
- 液压电磁是工业系统中关键的电机组件.
- 门故障是由电气问题,磨损,污染或组装错误引起的,导致停机和安全危险.
研究的目的:
- 开发一种非破坏性诊断技术,以快速准确地识别液压电磁故障.
- 为了减轻与门故障相关的系统停机时间和安全风险.
主要方法:
- 一个基于数据的模型,利用正常和故障门的电压和电流信号.
- 实现一个卷积式自动编码器用于特征提取.
- 基于超球的聚类用于故障分类和识别的应用.
主要成果:
- 诊断模型在分类七种不同的故障模式方面实现了98%的准确率.
- 该方法成功地确定了已知和潜在的新故障类型.
- 用白噪声增强数据证明了模型的稳定性.
结论:
- 拟议的诊断方法提供了一个有效的解决方案,用于准确和快速识别液压电磁的故障.
- 这项技术可以显著提高液压系统的维护和运行安全性.
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