齿轮箱作为风力机组的核心部件,故障频发,研究风机齿轮箱的故障诊断方法意义重大。针对最近邻(KNN)诊断方法对离群噪声不敏感和诊断精度较低的缺陷,提出了基于小波包和改进核最近邻算法的风机齿轮箱故障诊断方法。该方法应用小波包分析技术对故障特征进行提取,利用互近邻准则将故障数据集中的离群噪声点剔除,构建出基于核空间的改进型最近邻分类决策规则来识别齿轮箱的故障类型。试验表明:该方法可以有效地提升故障诊断精度和鲁棒性,为智能诊断技术的研究提供新思路。

As the core component of wind turbines, gearboxes frequently fail. It is significant to study the fault diagnosis methods of the wind turbine gearboxes. Considering that the Knearest neighbors (KNN) diagnosis method was insensitive to noise and the accuracy of fault diagnosis was low, a fault diagnosis method based on wavelet packet and improved kernel Knearest neighbors algorithm was proposed. This method used wavelet packet analysis technology to extract the fault features, and eliminated the noise by mutual nearest neighbor criterion. Then, an improved Knearest neighbors classification decision rule based on kernel method was established. Experiments showed that this method could effectively improve fault diagnosis accuracy and robustness, and provide new ideas for the research of intelligent diagnosis technology.

TM 315； TP 29

国家自然科学基金项目(11302123)；上海市浦江人才计划(15PJ1402500)