风力轮机的故障诊断使用基于一次性学习的图形神经网络模型
Shuai Yang1, Yifei Zhou2, Xu Chen2
1Chongqing Technology and Business University, National Research Base of Intelligent Manufacturing Service, Chongqing, People's Republic of China.
Royal Society open science
|July 7, 2023
概括
本研究介绍了一种新的风力轮机变速箱故障诊断模型,使用图形神经网络和一次性学习. 该方法有效地分类故障,即使有有限的振动数据,提高风力轮机的可靠性.
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:
- 风力轮机变速箱在恶劣的环境中工作,导致数据不足以进行有效的故障分类.
- 有限的数据可用性对风力轮机状况监测中的传统机器学习模型构成了重大挑战.
研究的目的:
- 开发一种故障诊断模型,能够使用有限的数据对风力轮机的变速箱故障进行分类.
- 通过先进的机器学习技术解决风力轮机状况监测数据稀缺的挑战.
主要方法:
- 提出了一个新的故障诊断模型,集成图形神经网络 (GNN) 和一次性学习.
- 一维的振动信号被转换成二维数据使用短时间里埃转换 (STFT).
- 从转换的数据中提取特征向量,以实现小样本学习.
主要成果:
- 提出的基于GNN的一次性学习模型在诊断风力轮机变速箱故障时实现了高分类准确性.
- 来自模拟风力轮机环境的实验结果验证了该模型的有效性.
- 该方法在与罗,匹配和原型网络相比显示出更高的性能.
结论:
- 开发的故障诊断模型有效地克服了风力轮机变速箱故障分类数据有限的挑战.
- 集成GNN和一次性学习为提高风力轮机的可靠性和维护提供了一个有希望的方法.
- 拟议的方法为现实世界风力轮机状况监测应用提供了可靠的解决方案.
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