风力轮行星变速箱的故障诊断方法基于改进的CNN CNN
Chenhua Xu1, Dan Liu2, Jian Cen2
1School of automation, Guangdong Polytechnic Normal University, Guangzhou, 510665, China. xchhelen@163.com.
Scientific reports
|September 12, 2025
概括
这项研究介绍了一种适应性的CNN模型,用于在风力轮机中可靠的行星变速箱故障诊断. 该方法的准确度超过95%,提高了设备的可靠性,并防止故障.
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:
- 风力轮机中的行星变速箱在复杂的环境中容易发生故障,导致设备损坏和传输故障.
- 准确和实时的故障诊断对于保持风力轮机的运行完整性和防止灾难性故障至关重要.
研究的目的:
- 提出一种自适应的卷积神经网络 (CNN) 模型,以便更好地识别行星变速箱故障.
- 为了提高风力轮机变速箱故障检测的实时性能和诊断准确性.
主要方法:
- 集体实证模式分解 (EEMD) 用于信号缩放以解决模态混合.
- 格拉米安角差异场 (GADF) 将处理的数据转换为图像,用于CNN输入.
- 开发了一种具有并行网络和适应参数的多层CNN结构,通过改进的Pied Kingfisher优化 (PKO) 算法进行优化.
主要成果:
- 由于适应性参数,拟议的模型实现了实时故障诊断功能.
- 诊断准确度超过95%,样本比例相似,不同样本比例超过85%.
- 这种方法显著提高了行星变速箱故障识别的可靠性.
结论:
- 开发的自适应CNN模型有效地诊断风力轮机中的行星变速箱故障.
- 整合EEMD,GADF和PKO优化可以提高诊断准确性和实时性能.
- 这种方法为提高风力轮机变速箱的运行可靠性提供了强大的解决方案.
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