Application of Gaussian Process Regression for Life Prediction of A Robot Based on RMS Torque Data of Axis Motors

This paper presents the results of the lifetime prediction of a six-axis robot using Gaussian process regression applied to the root mean square (RMS) torque data of each axis motor across its operating cycles. Three types of functions of the mean values were formulated and compared to construct an optimal Gaussian process regression model encapsulating the RMS torque values over these operating cycles. Contrary to monotonic increment, the RMS torque values throughout the operating cycles demonstrated a quadratic trend characterized by incremental escalation after a preceding decrement. The viability of employing Gaussian process regression for predicting the lifespan of robots was evaluated. This evaluation encompassed a comparison between the anticipated remaining useful life derived through Gaussian process regression and the prognosis rendered by the particle filter - an established technique within the realm of lifespan prediction.