基于多重分形谱和支持向量机的风电机组行星齿轮箱故障诊断与研究

风电机组行星齿轮箱振动信号是一种典型的非平稳、非线性信号，传统故障检测方法对于此类信号处理能力有限。为了克服传统方法的不足，提高故障诊断能力，提出了一种基于多重分形谱和支持向量机相结合的故障检测方法。首先通过多重分形定义求取信号的多重分形谱。然后在多重分形谱中提取八个特征量。最后将特征量作为支持向量机的输入向量，实现了在不同转速情况下对正常信号和四种太阳轮故障信号的分类与识别。实验结果证实了所提方法对行星齿轮箱信号特征进行提取是有效的，在不同转速情况下均提高了故障识别率。

Diagnosis and research of wind turbine planetary gearbox faults based on multifractal spectrum support vector machine (SVM)

The vibration signal of wind turbine is a typical kind of signal with nonstationary and nonlinear properties. The ability of traditional methods to process the kind of signal is limited. In order to solve the shortage of the traditional methods and improve the ability of fault diagnosis, this paper proposes a new method to detect the fault based on the multifractal and support vector machine (SVM). Firstly, the multifractal spectrum of the input signal is calculated through the definition of multifractal. And then the eight fractal characteristics of signal are extracted. Finally, taking the characteristics as the input vector of SVM, it achieves the classification and recognition of normal signal and fault signals of four kinds of sun gears under different rotation speed. The experimental result confirms that the proposed method can effectively extract the characteristics of the planetary gearbox signal, and can raise the fault recognition rate under the condition of different rotational speed. This work is supported by National Natural Science Foundation of China (No. 51507098 and No. 51507100), Shanghai Talent Development Fund (No. 201365), Science and Technology Commission of Shanghai (No. 15YF1404600 and No. 13DZ2251900 and No. 10DZ2273400) and Shuguang Program (No. 15SG50).