基于层次化混合分类器的含未知故障风机轴承故障诊断方法

为提高风机轴承故障诊断精度，针对含未知类型故障信号的误识别问题，提出一种风机轴承故障诊断新方法。首先，将风机轴承振动信号进行经验小波变换（EWT），对分解得到的固有模态分量（IMF）提取15种时-频域特征，构建特征向量集；然后，通过基尼（Gini）指数评价特征分类能力，构建最优特征集合；最后，采用单类支持向量机（OCSVM）与极限学习机（ELM）组合的层次化混合分类器进行故障诊断。对比单纯采用ELM、SVM分类器，新方法能够更好辨识含未知故障类型的风机轴承故障信号。

Fault Diagnosis Method of Wind Turbine Bearing with Unknown Fault based on Hierarchical Hybrid Classifier

In order to improve the diagnosis accuracy of wind turbine bearing fault signals, a new fault diagnosis method for wind turbine bearing was proposed to solve the problem of misidentification of unknown fault signals. Firstly, the vibration signals of wind turbine bearings were processed by empirical wavelet transform, and 15 time-frequency domain features were extracted from the decomposed inherent mode components to form a feature vector set. Then, the feature classification ability was evaluated by Gini index, and the optimal feature set was constructed. Finally, a hierarchical hybrid classifier combining single-class support vector machine and extreme learning machine was used for fault diagnosis. Compared with ELM and SVM classifier, the new method can identify the wind motor bearing fault signals with unknown fault types well.