基于主成分分析的齿轮箱故障特征融合分析

为有效降低齿轮箱故障特征的维数并提高诊断准确率，提出了基于主成分分析法的齿轮箱故障特征融合方法，并结合支持向量机和BP神经网络对诊断的准确率进行了分析。以齿轮箱中不同裂纹齿轮为对象，选取能够表征齿轮箱故障状态的时域、频域和基于希尔伯特变换的36个特征，提取累积贡献率达到95%以上的主成分并输入支持向量机分类器中进行分类识别，用BP神经网络分类器进行结果的比较分析。结果表明，采用主成分分析法与支持向量机相结合的方法，既能降低特征维数，降低计算的复杂性，又能有效地表征齿轮箱的运行状态，识别不同裂纹水平的齿轮，比单独使用支持向量机分类器的方法诊断准确率更高，训练时间更短。

Gearbox Fault Feature Fusion Based on Principal Component Analysis

To effectively reduce the dimension of gearbox fault feature and improve the accuracy of diagnosis, a fault signal feature fusion method of gearbox was proposed based on principal component analysis, and the support vector machine and BP neural network were used to analyze the diagnosis accuracy. The 36 features with different crack gears in gearbox were selected based on time-domain, frequency-domain and Hilbert transform, which could be used to characterize the fault states of gearbox. The principal components which had more than 95% cumulative contribution rate were extracted and input into support vector machine classifier for identification. BP neural network classifier was used for comparative analysis of the results. Results show that a combination of principal component analysis and support vector machine method can reduce the feature dimension and computational complexity, characterize the gearbox running status effectively, and identify the different levels of gear crack. The diagnosis accuracy is higher and the training time is shorter than that of individual support vector machine classifiers.