A Study on the Predictive Maintenance Algorithms Considering Load Characteristics of PMSMs to Drive EGR Blowers for Smart Ships

Abstract

Exhaust gas recirculation (EGR) is a NOx reduction technology that can meet stringent environmental regulatory requirements. EGR blower systems must be used to increase the exhaust gas pressure at a lower rate than the scavenging air pressure. Electric motor drive systems are essential to rotate the EGR blowers. For the effective management of the EGR blower systems in smart ships, there is a growing need for predictive maintenance technology fused with information and communication technology (ICT). Since an electric motor accounts for about 80% of electric loads driven by the EGR, it is essential to apply the predictive maintenance technology of the electric motor to maximize the reliability and operation time of the EGR blower system. Therefore, this paper presents the predictive maintenance algorithm to prevent the stator winding turn faults, which is the most significant cause of the electrical failure of the electric motors. The proposed algorithm predicts the remaining useful life (RUL) by obtaining the winding temperature value by considering the load characteristics of the electric motor. The validity of the proposed algorithm is verified through the simulation results of an EGR blower system model and the experimental results derived from using a test rig.