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首页> 外文期刊>Mechanical systems and signal processing >Gear fault detection using artificial neural networks and support vector machines with genetic algorithms
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Gear fault detection using artificial neural networks and support vector machines with genetic algorithms

机译：使用人工神经网络和带有遗传算法的支持向量机进行齿轮故障检测

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摘要

A study is presented to compare the performance of gear fault detection using artificial neural networks (ANNs) and support vector machines (SMVs). The time-domain vibration signals of a rotating machine with normal and defective gears are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to both classifiers based on ANNs and SVMs for two-class (normal or fault) recognition. The number of nodes in the hidden layer, in case of ANNs, and the radial basis function kernel parameter, in case of SVMs, along with the selection of input features are optimised using genetic algorithms (GAs). For each trial, the ANNs and SVMs are trained with a subset of the experimental data for known machine conditions. The trained ANNs and SVMs are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a gearbox. The roles of different vibration signals, obtained under both normal and light loads, and at low and high sampling rates, are investigated. The results compare the effectiveness of both types of classifiers without and with GA-based selection of features and the classifier parameters. For most of the cases considered, the classification accuracy of SVM is better than ANN, without GA. With GA-based selection, the performance of both classifiers are comparable, in most cases, with three selected features. However, for SVMs with six features, 100% classification success is achieved in all test cases. The training time of SVMs is substantially less compared to ANNs in all cases considered. The present classification accuracy compares well with the results reported in a recent work, (Mech. Systems Signal Process. 16 (2002) 373), though the data and the feature sets are different.
机译：提出了一项研究，以比较使用人工神经网络（ANN）和支持向量机（SMV）进行的齿轮故障检测的性能。处理具有正常齿轮和故障齿轮的旋转机械的时域振动信号以进行特征提取。从原始和预处理信号中提取的特征用作基于ANN和SVM的两个分类器的输入，以进行两类（正常或故障）识别。使用遗传算法（GA），可以在ANN的情况下隐藏层中的节点数，在SVM的情况下可以使用径向基函数核参数，并可以选择输入特征。对于每个试验，都使用已知机器条件的部分实验数据来训练ANN和SVM。使用剩余的数据集对经过训练的ANN和SVM进行测试。使用齿轮箱的实验振动数据说明了该过程。研究了在正常和轻载以及低采样率和高采样率下获得的不同振动信号的作用。结果比较了两种分类器在不使用基于遗传算法的特征和分类器参数的情况下的有效性。对于大多数考虑的情况，不使用GA，SVM的分类精度要优于ANN。使用基于GA的选择，在大多数情况下，两个分类器的性能在三个选定功能上是可比的。但是，对于具有六个功能的SVM，在所有测试用例中都可以实现100％的分类成功。在所有考虑的情况下，与人工神经网络相比，支持向量机的训练时间明显更少。尽管数据和特征集不同，但目前的分类准确度与最近的工作（Mech。Systems Signal Process。16（2002）373）中报告的结果具有很好的比较。

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