Detection and analysis of shaft misalignment in application of production and logistics systems using motor current signature analysis

• Published in: Expert systems with applications 2023-05, Vol.217, p.119463, Article 119463
• Main Authors: Lee, Junho, Lee, Younghun, Kim, Namsu
• Format: Article
• Language: English
• Subjects: Interior permanent magnet synchronous motor (IPMSM); Logistics system; Motor current signal analysis (MCSA); Prognostics and health management (PHM); Shaft misalignment; Smart manufacturing
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2022.119463

•We propose a method for detecting the misalignment of shaft connected to the motor.•This method enables efficient system management by condition-based maintenance.•Current data on frequency domain are useful for detecting the misalignment of shaft.•The method can detect the misalignment as well as its severity.•The method was proposed based on simulation results and it was experimentally verified. With the increasing demand for automated manufacturing and logistics system, interior permanent magnet synchronous motors (IPMSMs) are being actively researched because of their high torque density and efficiency. Consequently, fault diagnosis technology for electric motors is important for detecting abnormal signs of motors and evaluating the fault type and its severity. It enables condition-based maintenance of the main drive power of smart manufacturing as well as logistics system. Among the different types of faults in an IPMSM, shaft misalignment can cause various problems, including noise, vibration, and torque ripple, and shorten the life of the motor and gear connected to the shaft of the motor. This study investigates the fault characteristics of parallel misalignment, focusing on the characteristics of the frequency domain and size of the load fluctuation. The modeling and simulation for a drive system consisting of an IPMSM controlled by an inverter were carried out. Experiments were conducted to verify the simulation results and validate the effectiveness of the proposed method. The results show that motor current signal analysis can be used to detect misalignment faults under various conditions. We found that shaft misalignment generates abnormal current signals, which are correlated with fault severity, in the frequency domain.