An Improved Power Factor Correction Converter With AI-Powered Predictive Maintenance for PMSM Drives

Abstract

In this work, an advanced PFC converter was designed and AI-based predictive maintenance (PM) module was suggested to be included in complex PMSM drives.In this paper, an advanced PFC converter was designed and the AI based predictive maintenance (PM) module was proposed to be added in a complex PMSM drives system. The power quality of the PFC stage is good and reduces the generation of harmonic distortion caused by PWM based soft switching and effectively controls the power factor close to unity by using the capacitor bank controls, and noise suppression. The converter, motor drive, communication and data processing is controlled by a microcontroller. The use of a multi-sensor approach, which uses a three-axis accelerometer for vibration monitoring, a current sensor for electrical fault detection and a temperature sensor for thermal condition assessment, is used in the predictive maintenance system. These are then inputted into machine-learning algorithms, that detect unabnormal vibration, temperature and current fluctuations, which happen at the occurrence of faults such as bearing wear, rotor misalignment, inverter switching problems, thermal stress and so on. The integrated solution provides greater efficiency and reliability of the PMSM drives, increased equipment life and maintenance effectiveness, reduced unexpected downtime and operating costs.