A Remaining Useful Life Estimation Method for Freeway Electromechanical Power Supply Equipment Based on Sensitive Parameters

Readily available power supply equipment is necessary to ensure safe and stable operation of freeway electromechanical systems. Accurate estimation of equipment remaining useful life (RUL) is critical to ensuring readily available power equipment. This paper proposes a RUL estimation method for freeway electromechanical power supply equipment. Based on equipment operating status monitoring data, we make a multi-angle analysis of degradation by constructing a multi-dimensional parameter set and selecting sensitive parameters from the set. We estimate the power supply equipment RUL by combining multi-variable gray model [MGM (1, n)] and radial basis neural network (RBFNN). MGM(1, n) is utilized to predict the trends of sensitive parameters and RBFNN is utilized to establish a mapping relationship between sensitive parameters and RUL. Experiments performed on monitoring data of UPS power supply battery pack shows a low test root mean square error of 0.046, which verifies the effectiveness and accuracy of the method.