离心故障诊断的智能框架利用波段一致性分析和深度学习来进行离心故障诊断
Niamat Ullah1, Zahoor Ahmad1, Muhammad Farooq Siddique1
1Department of Electrical, Electronics, and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
本研究介绍了使用波波连贯性分析和深度学习进行离心故障诊断的智能框架. 该方法通过分析振动信号,准确地识别故障,改进现有技术.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 离心 (CP) 故障诊断受到减弱的振动信号和噪声的挑战.
- 传统的方法需要大量的人类专业知识来提取特征.
- 开发自主故障诊断对于工业应用至关重要.
研究的目的:
- 提出一个智能框架,用于CP的自主故障诊断.
- 在杂的环境中克服传统统计特征的局限性.
- 提高CP中故障检测的灵敏度和准确性.
主要方法:
- 波形连贯性分析 (WCA) 用于处理对健康基线的振动信号.
- 卷积神经网络 (CNN) 和卷积自编码器 (CAE) 用于自主从连接图中提取特征.
- 人工神经网络 (ANN) 用于使用组合特征对健康状况进行分类.
主要成果:
- 该框架成功地自主提取了敏感的健康信息.
- 整合图有效地代表了CP对故障的脆弱性.
- 集成的DL模型在故障识别中实现了高精度.
结论:
- 拟议的WCA和DL框架为CP故障诊断提供了一个强大的解决方案.
- 这种方法在准确性方面超过现有的方法,用于真实世界的工业数据.
- 自主功能提取显著减少了对专家知识的依赖.
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