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首页> 外文期刊>Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability >Wear detection of rolling element bearings using multiple-sensing technologies and mixture-model-based clustering method
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Wear detection of rolling element bearings using multiple-sensing technologies and mixture-model-based clustering method

机译：滚动轴承的多传感器磨损检测和基于混合模型的聚类方法

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摘要

Online fault diagnostic technologies are fast emerging for detection of incipient faults on tribological components to avoid catastrophic failure. Vibration analysis has long been used to detect machine faults, but is sensitive to relatively severe conditions only. Electrostatic monitoring is a newly developed approach with the potential to detect precursor processes that indicate contact distress and wear. Recently, at the University of Southampton, both vibration and electrostatic sensors were implemented on a bearing testing rig to evaluate their effectiveness in detecting bearing faults. The results indicate that both types of sensor are sensitive to bearing deterioration shortly before complete failure. However, univariate plots of signals from both types of sensor only exhibit significant change when entering the severe wear stage. Therefore, multivariate techniques for detecting wear severity of components at different running stages need investigating. In this study, an unsupervised training method, called mixture-model-based clustering, that utilizes the expectation maximization (EM) algorithm is employed to develop further a wear detection technique. The choice and extraction of significant features from both vibration and electrostatic sensors are discussed as step one. The second step uses the clustering method to examine the behaviour of the extracted features during different running stages, and to quantify how good the sensors are at distinguishing wear severity. In the third step, a dynamic wear detection process is simulated. Clustering is applied to baseline data from a known healthy bearing and data from different wear stages to see if the data naturally group by wear condition. The result shows that the unsupervised clustering method is able not only to learn and detect wear conditions of the rolling element bearings with the developed statistical monitoring charts of occupation probability (OP) in the clusters and number of the trained clusters (NC), but also to obtain the advantage of detecting insignificant abnormalities that might be overlooked in the conventional plots.
机译：在线故障诊断技术正在迅速出现，用于检测摩擦学组件上的早期故障，从而避免灾难性故障。振动分析长期以来一直用于检测机器故障，但仅对相对恶劣的条件敏感。静电监测是一种新开发的方法，具有检测指示接触不良和磨损的前体过程的潜力。最近，在南安普敦大学，在轴承测试台上同时安装了振动传感器和静电传感器，以评估它们在检测轴承故障中的有效性。结果表明，两种类型的传感器在完全失效之前不久都对轴承的劣化敏感。但是，来自两种类型传感器的信号单变量图仅在进入严重磨损阶段时才显示出显着变化。因此，需要研究用于检测部件在不同运行阶段的磨损严重程度的多元技术。在这项研究中，采用了一种基于期望模型（EM）的无监督训练方法（称为基于混合模型的聚类）来进一步开发磨损检测技术。第一步讨论从振动和静电传感器中选择和提取重要特征。第二步使用聚类方法检查不同运行阶段中提取的特征的行为，并量化传感器在区分磨损严重程度方面的表现。第三步，模拟动态磨损检测过程。将聚类应用于来自已知健康轴承的基线数据和来自不同磨损阶段的数据，以查看数据是否自然地按磨损状况分组。结果表明，无监督聚类方法不仅能够利用已开发的聚类中的职业概率（OP）和训练的聚类数（NC）统计监测图来学习和检测滚动轴承的磨损状况，而且能够获得检测常规图中可能忽略的微小异常的优势。

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