AI-Enabled Metal-Polymer Plain Bearing Based on the Triboelectric Principle

Abstract

With the rapid development of the Internet of Things and artificial intelligence (AI), the requirement for sensing technologies for smart bearings has increased dramatically. The general bearing sensors can only recognize the basic information from temperature or vibration, far from satisfying the self-diagnosis and self-maintenance. Recently, self-powered sensing technologies based on triboelectric nanogenerators have paved a new route for fabricating smart bearings. In this study, the triboelectric principle is applied to a commercial metal-polymer plain bearing (MPPB) bearing, which can achieve self-sensing, self-diagnosis, and self-maintenance. The geometrical structure of the triboelectric MPPB (T-MPPB) is designed to balance the output efficiency and external load, and the super durability and load capability are verified. Besides, the mechanism behind the output change trend under boundary and hydrostatic fluid lubrication is revealed for the first time. Furthermore, the deep learning algorithm can classify the lubrication states with highly accurate performance. The proposed T-MPPB has the potential to achieve self-maintenance with the lubricating pump according to the lubrication condition classified by the AI. This research not only establishes the feasibility of designing self-powered smart MPPB but also demonstrates a way for identifying lubrication states, thus achieving self-diagnosis and self-maintenance ability by self-powered sensors.