Running-in real-time wear generation under vary working condition based on Gaussian process regression approximation

In real-time lubricant monitoring operation, the wear's generation inside the flow lubricant is identified as the lubricant's performance indicator. The online visual ferrograph (OLVF) based on magnetic deposition and image analysis could detect the wear during the running machine process. However, the time gap during OLVF's data processing makes the corrective actions of abnormal high wear rate are not based on time-sensitive data. Therefore, this study proposes a data-driven approach based on Gaussian process regression approximation to analyze the acquired data and thus create new knowledge on the understanding of real-time wear generation. Moreover, running-in processes with high seasonality (abnormal) data are being investigated to give insight into the initial process's phenomena. In this study, the combined temperature-controlled four-ball tribometer with an OLVF is employed to acquire debris data inside the flow channel, and the coefficient of friction (COF) represents the wear trend under varying working conditions. Results show that the experimental setup and the approach can be used to investigate running-in real-time wear generation.