Bearings are one of the most widely used components in the industry that are more vulnerable than other parts of machines. In this research, a precise method was developed for diagnosis bearing detection based on vibrating signals. Vibration signals were recorded from four common faults in the bearings at three speeds of 1800, 3900, and 6600 rpm. The vibration signals were transmitted by the fast Fourier transform to the frequency domain. A total of 24 features were extracted from frequency and time signals. The superior features are selected using the combination of genetic algorithm and artificial neural network. A support vector machine is used to intelligently detect ball bearing faults. The accuracy of the support vector machine with all extracted features in different revolutions showed that the highest accuracy for training and test data was obtained 78.86% and 69.33% respectively, at 1800 rpm. The results of reduction and selection of superior features showed that the highest accuracy of the support machine was obtained in the classification of ball bearing faults for training and test data 97.14% and 93.33%, respectively. The results show that the use of the feature selection method based on the genetic algorithm will increase the accuracy of the classification.

中文翻译：

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遗传算法与支持向量机相结合的球轴承故障状态智能监测

轴承是行业中使用最广泛的部件之一，比机器的其他部件更容易受到损坏。在这项研究中，开发了一种基于振动信号诊断轴承检测的精确方法。以 1800、3900 和 6600 rpm 三种速度记录轴承中四个常见故障的振动信号。振动信号通过快速傅立叶变换传输到频域。从频率和时间信号中总共提取了 24 个特征。使用遗传算法和人工神经网络相结合的方法来选择优越的特征。支持向量机用于智能检测滚珠轴承故障。支持向量机在不同转速下所有提取特征的精度表明，在 1800 rpm 时，训练和测试数据的最高精度分别为 78.86% 和 69.33%。优特征的约简和选择结果表明，在训练和测试数据的球轴承故障分类中，支撑机的最高准确率分别为97.14%和93.33%。结果表明，采用基于遗传算法的特征选择方法会提高分类的准确率。分别为 14% 和 93.33%。结果表明，采用基于遗传算法的特征选择方法会提高分类的准确率。分别为 14% 和 93.33%。结果表明，采用基于遗传算法的特征选择方法会提高分类的准确率。