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Multiclass fault diagnosis in gears using support vector machine algorithms based on frequency domain data

机译：基于频域数据的支持向量机算法在齿轮故障诊断中的应用

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摘要

As a dominant machine learning method, the support vector machine is known to have good generalization capability in its application of the multiclass machine-fault classification utility. In this paper, an application of the SVM in multiclass gear-fault diagnosis has been studied when the gear vibration data in frequency domain averaged over a large number of samples is used. It is established that the SVM classifier has excellent multiclass classification accuracy when the training data and testing data are at identical angular speeds. However, this method relies on the availability of both the training and testing data at that particular angular speed of the gear operation. But the training data may not always be available at all angular speeds of the gear. Hence, two novel techniques, namely the interpolation and the extrapolation methods, have been proposed; these techniques that help the SVM classifier perform multiclass gear fault diagnosis with noticeable accuracy, even in the absence of the training data at the testing angular speed. This method is based on interpolating and extrapolating the training data at angular speeds near the speeds of the test data. In this study effects of choice over different kernels and parameters of SVM on its overall classification accuracy has been studied and optimum values for these are suggested. Finally, the effect on length of training data and data density on the SVM accuracy is also presented.
机译：作为一种占主导地位的机器学习方法，已知支持向量机在多类机器故障分类实用程序的应用中具有良好的泛化能力。本文研究了支持向量机在多样本齿轮故障诊断中的应用。可以确定的是，当训练数据和测试数据在相同的角速度下时，SVM分类器具有出色的多类分类精度。但是，该方法依赖于在齿轮操作的特定角速度下训练和测试数据的可用性。但是训练数据可能并不总是在齿轮的所有角速度下都可用。因此，提出了两种新颖的技术，即内插法和外推法。这些技术可帮助SVM分类器以明显的准确性执行多类齿轮故障诊断，即使在测试角速度下没有训练数据的情况下也是如此。该方法基于以接近测试数据速度的角速度内插和外推训练数据。在这项研究中，研究了支持向量机的不同内核和参数的选择对其总体分类精度的影响，并为这些建议提供了最佳值。最后，还介绍了训练数据长度和数据密度对SVM准确性的影响。

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