The support vector machine (SVM) does not have a fixed parameter selection method and the manual selection of parameters is difficult to determine the validity, which affects the accuracy of recognition. simultaneously, The existing coarse-grained approach cannot effectively analyze the high-frequency components of time series. In view of the shortcomings of the above method, we put forward a new technique of rolling bearings for fault detection, which combines wavelet packet dispersion entropy (WPDE) and artificial fish swarm algorithm (AFSA) optimize support vector machines (AFSA-SVM). First of all, wavelet packet is devoted to decompose the original vibration signal into components of different frequency bands. Secondly, the dispersion entropy (DE) are calculated for each of the obtained frequency band components to acquire more comprehensive and complete fault information. Afterward, Input feature samples into the SVM model for training, and AFSA is used to optimize the parameters of SVM to obtain the optimal value so as to establish the best classification model. Finally, the prepared test set is input into AFSA-SVM for fault classification. The achievement of bearing detection experiments show that this approach can accurately and quickly identify fault types.

支持向量机（SVM）没有固定的参数选择方法，参数的手动选择难以确定有效性，从而影响识别的准确性。同时，现有的粗粒度方法无法有效地分析时间序列的高频成分。针对上述方法的不足，提出了一种新的滚动轴承故障检测技术，该技术结合了小波包色散熵（WPDE）和人工鱼群算法（AFSA）优化支持向量机（AFSA-SVM）。首先，小波包致力于将原始振动信号分解为不同频段的分量。其次，为获得的每个频带分量计算色散熵（DE），以获取更全面和完整的故障信息。然后，将特征样本输入到SVM模型中进行训练，然后使用AFSA优化SVM的参数以获得最优值，从而建立最佳分类模型。最后，将准备好的测试集输入到AFSA-SVM中以进行故障分类。轴承检测实验的结果表明，该方法可以准确，快速地识别故障类型。