Investigation of defects in roll contacts of machine elements with Acoustic Emission and Unsupervised Machine Learning
Investigation of defects in roll contacts of machine elements with Acoustic Emission and Unsupervised Machine Learning
复制标题
DOI：
10.1088/1757-899x/1193/1/012085
复制
发表时间：
2021-10
期刊：
影响因子：
--
通讯作者：
J. Hillenbrand;J. Detroy;J. Fleischer
中科院分区：
文献类型：
--
作者：
J. Hillenbrand;J. Detroy;J. Fleischer
在工业4.0和工业物联网时代，机器越来越多地连接在一起，从而实现持续监控。来自机器和安装的传感器的各种信息用于开发状态监测解决方案。这些系统用于防止过早的故障和由于机器停机相关的后续成本。该领域的最新研究应用了监督机器学习，从捕获的信号中提取特征并训练分类器。监督学习方法需要大量的标记数据，这些数据的生成耗时且需要领域知识。为此，本工作采用无监督学习方法来区分轴向球轴承的不同缺陷和运行状态。在这项工作的范围内，记录和评估超声波范围内的声发射（AE）测量。在轴向轴承的滚动接触中播种了人为缺陷。从声发射信号中提取出一系列最先进的特征。然后，使用无监督过滤算法拉普拉斯分数（Laplacian Score）来选择最显著的特征。随后，采用DBSCAN聚类算法对现有损伤进行归纳。
In the age of Industry 4.0 and IIoT machines are becoming increasingly connected enabling continuous monitoring. A variety of information from machines and installed sensors is used to develop condition monitoring solutions. These systems are used to prevent premature failures and the follow-up costs due to machine downtime associated with them. Recent research in this area applies supervised machine learning, extracting features from captured signals and training classifiers. Supervised learning approaches require large amounts of labeled data, whose generation is time consuming and requires domain knowledge. For this reason, an unsupervised learning approach is being used in this work to distinguish between different defect and operation states of axial ball bearings. Within the scope of this work, acoustic emission (AE) measurements in the ultrasonic range are recorded and evaluated. Artificial defects are seeded in the rolling contact of axial bearings. From the AE signals a selection of state-of-the-art features is extracted. Then, the Laplacian Score, an unsupervised filter algorithm, is used to select the most significant features. Subsequently, the DBSCAN clustering algorithm is used to draw conclusions about the existing damage.