针对风力发电机齿轮箱故障振动信号的非平稳性、特征混叠和诊断正确率低等问题,提出一种基于图注意力网络（GAT）的风力发电机齿轮箱故障诊断方法。首先利用原始振动信号的频谱定义节点和边,将故障信号构造为可视图;然后将可视图数据作为输入,在GAT中嵌入邻居自注意力机制使其自适应提取可视图信号的节点特征和结构特征;最后使用分类器对提取的节点特征进行分类识别。通过行星齿轮箱数据集和风力发电机齿轮箱数据集的实验,结果表明与机器学习、深度学习以及其他图神经网络相比,所提方法准确率更高,鲁棒性和抗噪性更好,可有效实现端到端的智能故障诊断。
Abstract
Aiming at the issues of non-stationary, feature aliasing and low diagnostic accuracy of fault vibration signals from wind turbine gearbox, a fault diagnosis method for wind turbine gearbox based on graph attention networks （GAT） is proposed. Firstly, nodes and edges are defined by the frequency spectrum of the raw vibration signal to construct a visibility graph. Then, taking visibility graph data as input, the neighbor self-attention mechanism is embedded in GAT to adaptively extract node features and structure features of visibility graph signals. Finally, the classifier is used to classify and recognize the extracted node features. The experimental results of planetary gearbox dataset and wind turbine gearbox dataset show that the proposed method has higher accuracy, better robustness and noise immunity than machine learning, deep learning and other graph neural networks, which can effectively achieve end-to-end intelligent fault diagnosis.
关键词
风力发电机 /
齿轮箱 /
故障诊断 /
可视图 /
图注意力网络
Key words
wind turbines /
gearbox /
fault diagnosis /
visibility graph /
graph attention networks （GAT）
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基金
新疆维吾尔自治区自然科学基金青年项目（2022D01C89）; 国家自然科学基金（52065064; 51967019）; 天山青年计划（2020Q066）
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