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Application of multi-class fuzzy support vector machine classifier for fault diagnosis of wind turbine

机译：多类模糊支持向量机分类器在风机故障诊断中的应用

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This paper presents an approach for fault diagnosis of wind turbine (WT) based on multi-class fuzzy support vector machine (FSVM) classifier. In this method, empirical mode decomposition is adopted to extract fault feature vectors from vibration signals. FSVM is used for solving classification problem with outliers or noises, where kernel fuzzy c-means clustering algorithm and particle swarm optimization algorithm are applied to calculate fuzzy membership and optimize the parameters of kernel function of FSVM, respectively. In addition, to study the performance of the proposed approach, another two widely used methods, named back propagation neural network and standard support vector machine, are studied and compared. Discrete wavelet transform is also used to extract fault feature vectors. To validate the proposed approach, a direct-drive WT test rig is constructed and the experiments are carried out. The experimental results show that the proposed approach is an effective fault diagnosis method for WT, which has a better performance and can achieve higher diagnostic accuracy. (C) 2015 Elsevier B. V. All rights reserved.
机译：本文提出了一种基于多类模糊支持向量机（FSVM）分类器的风机故障诊断方法。该方法采用经验模态分解从振动信号中提取故障特征向量。 FSVM用于解决具有离群值或噪声的分类问题，其中分别应用核模糊c均值聚类算法和粒子群优化算法来计算模糊隶属度并优化FSVM核函数的参数。此外，为了研究该方法的性能，还研究并比较了另外两种广泛使用的方法，即反向传播神经网络和标准支持向量机。离散小波变换也用于提取故障特征向量。为了验证所提出的方法，建造了直接驱动的WT测试台并进行了实验。实验结果表明，该方法是一种有效的WT故障诊断方法，具有较好的性能，可以达到较高的诊断精度。 （C）2015 Elsevier B. V.保留所有权利。

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