Bearing faults account for approximately half of all electric machine failures. Bearing condition monitoring is of practical importance. Until now, there is not any complete research on application of frequency domain signal features on bearing natural fault prediction and also application of ANFIS fuzzy systems for natural bearing fault classification, especially using acoustic emission signals. So, this paper focuses on the study of natural fault detection of angular contact ball bearing using frequency domain signal processing based on acoustic emission signals. This study consists of three stages. At the first stage, after recording the acoustic emission waveforms from the experimental test rig, 45 frequency domain features are introduced and calculated. Next, principal components of features are computed using the PCA and FDA methods. Finally, Adaptive Neuro-Fuzzy Inference System (ANFIS) is used to classify the healthy and naturally defected bearings based on principal components of PCA and FDA methods and the accuracy of these methods are compared with each other. The results show that the classification using the ANFIS network based on the FDA features has less error compared to the features extracted from the PCA method. The classification error using the FDA features has its lowest value using the input pimf membership function and the hybrid optimization method for the constant output membership function, which is 9.0352 × 10⁻⁹. The ANFIS accuracy for the first principal component is 100%. The Anfis accuracy for second and third principal component for PCA and FDA methods are 67.6%, 59.15%, 64.8% and 56.3%, respectively.

轴承故障约占所有电机故障的一半。轴承状态监测具有实际意义。迄今为止，还没有对将频域信号特征应用于轴承自然故障预测中的应用以及将ANFIS模糊系统应用于自然轴承故障分类（尤其是使用声发射信号）进行完整的研究。因此，本文重点研究基于声发射信号的频域信号处理对角接触球轴承的自然故障检测。这项研究包括三个阶段。在第一阶段，在记录了来自实验测试台的声发射波形后，引入并计算了45个频域特征。接下来，使用PCA和FDA方法计算特征的主要成分。最后，自适应神经模糊推理系统（ANFIS）用于基于PCA和FDA方法的主要成分对健康和自然缺陷轴承进行分类，并将这些方法的准确性进行比较。结果表明，与从PCA方法提取的特征相比，使用基于FDA特征的ANFIS网络进行分类的错误更少。使用FDA特征的分类误差使用输入pimf隶属函数和恒定输出隶属函数的混合优化方法的最小值为9.0352×10 结果表明，与从PCA方法提取的特征相比，使用基于FDA特征的ANFIS网络进行分类的错误更少。使用FDA特征的分类误差使用输入pimf隶属函数和恒定输出隶属函数的混合优化方法的最小值为9.0352×10 结果表明，与从PCA方法提取的特征相比，使用基于FDA特征的ANFIS网络进行分类的错误更少。使用FDA特征的分类误差使用输入pimf隶属函数和恒定输出隶属函数的混合优化方法的最小值为9.0352×10^-9。第一个主成分的ANFIS精度为100％。PCA和FDA方法的第二和第三主要成分的Anfis准确度分别为67.6％，59.15％，64.8％和56.3％。