ABSTRACT Recently, precise and deterministic feature extraction is one of the current research topics for bearing fault diagnosis. For this aim, an experimental bearing test setup was created in this study. In this setup, vibration signals were obtained from the bearings on which artificial faults were generated in specific sizes. A new feature extraction method based on co-occurrence matrices for bearing vibration signals was proposed instead of the conventional feature extraction methods, as in the literature. The One (1) Dimensional–Local Binary Patterns (1D-LBP) method was first applied to bearing vibration signals, and a new signal whose values ranged between 0–255 was obtained. Then, co-occurrence matrices were obtained from these signals. The correlation, energy, homogeneity, and contrast features were extracted from these matrices. Different machine learning methods were employed with these features to carry out the classification process. Three different data sets were used to test the proposed approach. As a result of analysing the signals with the proposed model, the success rate is 87.50% for dataset1 (different speed), 96.5% for dataset2 (fault size (mm)) and 99.30% for dataset3 (fault type – inner ring, outer ring, ball) was found, respectively.

中文翻译：

---

基于一维灰度共生矩阵的轴承故障分类特征提取新方法

摘要 近年来，精确且确定性的特征提取是当前轴承故障诊断的研究课题之一。为此，本研究创建了一个实验性轴承测试装置。在此设置中，振动信号是从产生特定尺寸人为故障的轴承中获得的。提出了一种新的基于轴承振动信号共生矩阵的特征提取方法，而不是文献中的传统特征提取方法。一 (1) 维-局部二元模式 (1D-LBP) 方法首先应用于轴承振动信号，并获得了值介于 0-255 之间的新信号。然后，从这些信号中获得共生矩阵。从这些矩阵中提取相关性、能量、同质性和对比度特征。针对这些特征采用不同的机器学习方法来执行分类过程。三个不同的数据集被用来测试所提出的方法。用所提出的模型分析信号的结果，数据集1（不同速度）的成功率为87.50％，数据集2（故障尺寸（mm））的成功率为96.5％，数据集3（故障类型-内环，外环）的成功率为99.30％ ，球）分别被发现。