Bearing diagnosis has attracted considerable research interest; thus, researchers have developed several signal processing techniques using vibration analysis to monitor the rotating machinery’s conditions. In practical engineering, features extraction with most relevant information from experimental vibration signals under variable operation conditions is still regarded as the most critical concern. Therefore, actual works focus on combining Time Domain Features (TDFs) with decomposition techniques to obtain accurate results for defect detection, identification, and classification. In this paper, a new hybrid method is proposed, which is based on Time Synchronous Averaging (TSA), TDFs, and Singular Value Decomposition (SVD) for the feature extraction, then the Adaptive Neuro-Fuzzy Inference System (ANFIS) which gathers the advantages of both neural networks and fuzzy logic is applied for the classification process. First, TSA is used to reduce noises in the vibration signal by extracting the periodic waveforms from the disturbed data; thereafter, TDFs are applied on each synchronous signal to construct a feature matrix; afterwards, SVD is performed on the obtained matrices to remove the instability of statistical values and select the most stable vectors. Finally, ANFIS is implemented to provide a powerful automatic tool for features classification.

轴承诊断引起了相当大的研究兴趣。因此，研究人员开发了几种使用振动分析来监测旋转机械状况的信号处理技术。在实际工程中，在可变操作条件下从实验振动信号中获取最相关信息的特征提取仍然被认为是最关键的问题。因此，实际工作着重于将时域特征（TDF）与分解技术相结合，以获得准确的结果，以进行缺陷检测，识别和分类。本文提出了一种新的混合方法，该方法基于时间同步平均（TSA），TDF和奇异值分解（SVD）进行特征提取，然后将神经网络和模糊逻辑两者都具有优势的自适应神经模糊推理系统（ANFIS）用于分类过程。首先，TSA用于通过从受干扰的数据中提取周期波形来减少振动信号中的噪声。之后，将TDF应用于每个同步信号以构建特征矩阵。之后，对获得的矩阵执行SVD运算以消除统计值的不稳定性，并选择最稳定的向量。最终，实现了ANFIS，以提供功能强大的自动特征分类工具。TDF应用于每个同步信号以构建特征矩阵；之后，对获得的矩阵执行SVD运算以消除统计值的不稳定性，并选择最稳定的向量。最终，实现了ANFIS，以提供功能强大的自动特征分类工具。TDF应用于每个同步信号以构建特征矩阵；之后，对获得的矩阵执行SVD运算以消除统计值的不稳定性，并选择最稳定的向量。最终，实现了ANFIS，以提供功能强大的自动特征分类工具。