Early detection of contact distress for enhanced performance monitoring and predictive inspection of machines

监测摩擦接触的健康状况需要研究摩擦、摩擦膜完整性和磨损转变。这些都对实验摩擦学家提出了挑战，以开发出准确的现场测量方法，并理想地进行连续监测。间接测量，如摩擦变化、突然加热、振动变化或油中的碎屑，可以检测到严重的磨损转变，但无法检测到接触不健康演变过程中发生的细微机械变化。然而，由摩擦接触产生的表面电荷由单个宏观传感器测量，已检测到摩擦学特征，如摩擦膜化学、粘着磨损、磨料磨损、相变和磨损碎片，但在较大的表面区域。因此，这项提议将使现有的传感技术微型化，使用嵌入式电子设备来克服信噪比问题，并使用阵列传感器进行增强传感和机器学习。将对传感器阵列/学习系统进行培训，以从表面的电荷图中检测出润滑接触衰变的早期证据，并允许更好地预测剩余使用寿命，或在运行条件下需要进行何种校正调整，以确保操作完整性。

Monitoring the health of tribocontacts requires the study of friction, tribofilm integrity, and wear transitions. These challenge experimental tribologists to develop accurate methods for in-situ measurements and ideally continuous monitoring. Indirect measurements such as friction changes, sudden heating, changes in vibration or debris in the oil can detect severe wear transitions but cannot detect the subtle mechanistic changes which occur in unhealthy evolution of the contact. However, surface charge generated by tribocontacts and measured by single macro sensors, has detected tribological features such as tribofilm chemistry, adhesive wear, abrasive wear, phase transformations and wear debris but over large surfaces areas. This proposal, therefore, will miniaturise existing sensing technology, with embedded electronics to overcome signal to noise issues, and use arrayed sensors for augmented sensing, and machine learning. The sensor array /learning system would be trained to detect early evidence of lubricated contact decay from charge maps of the surface and allow better prediction of remaining useful life or, what corrective adjustment is needed in running conditions, to assure operational integrity.