卡普兰水电发电机组的异常声音诊断基于连续波纹转换和转移学习
Yu Liu1, Zhuofei Xu1,2, Pengcheng Guo1
1School of Water Resources and Hydroelectric Engineering, Xi'an University of Technology, Xi'an 710048, China.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
本研究引入了一种新的方法来诊断水电发电机组的故障,使用连续波波变换 (CWT) 和转移学习 (TL). 该方法通过先进的降噪和基于图像的分析,显著提高了故障检测的准确性.
科学领域:
- 工程 工程师 工程师 工程师
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 水力发电机组是关键基础设施,需要持续监控.
- 诊断异常声音对于防止灾难性故障和确保运营效率至关重要.
- 现有的诊断方法在杂的环境中可能缺乏准确性或稳定性.
研究的目的:
- 开发一种准确有效的方法来诊断水电发电机组中的异常声音.
- 通过结合连续波波变换 (CWT) 和转移学习 (TL) 来提高故障检测能力.
- 根据已建立的深度学习模型验证拟议方法的有效性.
主要方法:
- 使用光谱噪声门技术的无声化算法被应用来增强故障信号.
- 连续波形转换 (CWT) 用于将声信号转换为伪色图像.
- 为了特征提取,开发了一个具有简化完全连接层的转移学习模型.
- 优化了关键信号处理参数,以提高性能.
主要成果:
- 提出的方法实现了高的诊断准确率,显著改善了预先过结果 (例如,从84.83%到98.88%).
- 降噪过程在增强故障特征方面非常有效.
- 对比分析表明,拟议的模型表现优于经典的深度学习模型,如AlexNet,Resnet18和MobileNetV3.
- 该方法证明了卡普兰水电发电机组在各种断层状态中的稳健故障诊断.
结论:
- 基于CWT和TL开发的方法为水力发电机组中异常声音诊断提供了高度准确的解决方案.
- 集成的降噪和基于图像的分析对于有效的故障检测至关重要.
- 这种方法对于水电发电机组的日常监测,维护和运行安全至关重要.
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