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Fault Detection of Rolling Element Bearings using Advanced Signal Processing Technique

机译：使用高级信号处理技术的滚动轴承故障检测

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In rotating machinery, rolling element bearings are one of the most critical components and a large majority of system failures arise from faulty bearings. Hence, there is an increasing demand to find an effective and reliable condition monitoring technique. In this paper, a procedure for detecting various types of bearing faults using thermal imaging is presented and assessed. Five different fault cases are tested: No Fault (NF), Line Fault (LF), Small Circle Fault (SCF), Double Line Fault (DLF), and Large Circle Fault (LCF). Experiments were conducted on the BENTLY NEVADA RK4 Rotor Kit. A novel signal processing algorithm is proposed to detect the faults which involves utilizing the Discrete Wavelet Transform (DWT). The HAAR wavelet is used as the mother wavelet and a decomposition level of 7 is used. The inverse of HAAR is applied on the decomposed signal and the envelop spectrum is plotted. A classifier is then created to identify the fault that utilizes the Support Vector Machine (SVM) classifier to extract the features and a confusion matrix is developed to detect the prediction accuracy.
机译：在旋转机械中，滚动轴承是最关键的组件之一，大多数系统故障是由故障轴承引起的。因此，对找到有效且可靠的状态监视技术的需求不断增加。在本文中，提出并评估了使用热成像技术检测各种类型轴承故障的过程。测试了五种不同的故障情况：无故障（NF），线路故障（LF），小圆圈故障（SCF），双线路故障（DLF）和大圆圈故障（LCF）。在BENTLY NEVADA RK4转子套件上进行了实验。提出了一种新颖的信号处理算法来检测故障，该算法涉及利用离散小波变换（DWT）。 HAAR小波用作母小波，分解级别为7。 HAAR的逆函数应用于分解后的信号，并绘制出包络谱。然后创建分类器以识别故障，该故障利用支持向量机（SVM）分类器提取特征，并开发出混淆矩阵来检测预测精度。

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