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.