离心的故障诊断:使用卷积自编码器和人工神经网络的双电平图方法
Wasim Zaman1, Zahoor Ahmad1, Jong-Myon Kim1,2
1Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
本研究介绍了使用信号处理和深度学习对离心的先进故障诊断方法. 这种新的方法在识别故障方面取得了很高的准确性,即使在不同的运行条件下.
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:
- 离心对于流体运输至关重要,但操作变化,如入口压力变化,可能会挑战传统的故障诊断.
- 现有的机器学习模型可能会因为受到动态操作条件影响的原始统计特征而难以概括.
研究的目的:
- 开发一种强大的离心故障诊断方法,适应不同输入流体压力的离心.
- 为了提高离心故障分类的准确性和可靠性.
主要方法:
- 来自离心的振动信号使用低通过,连续波量变换和斯托克威尔变换进行处理,以生成时间频率级图.
- 索贝尔波器增强了从刻度图中提取特征,然后使用并行卷积自动编码器进行特征学习.
- 提取的特征被合并并用于训练两层人工神经网络进行故障分类.
主要成果:
- 提出的方法实现了100%的高分类准确率,99.2%和98.8%在三个不同的数据集与不同的入口压力.
- 与故障诊断中的参考方法相比,深度学习方法显示出更高的性能.
结论:
- 综合信号处理和深度学习技术为离心故障诊断提供了高度准确和强大的解决方案.
- 这种方法有效地解决了不同输入流体压力的挑战,改善了诊断概括性.
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