Bearing fault detection of line start permanent magnet synchronous motor on scada environment by using online condition monitoring

Line Start Permanent Magnet Synchronous Motor (LSPMSM) production line systems, fan systems with the advantages of high efficiency and high power-factor replace the asynchronous motors day by day in industrial areas. LSPMS motors with mains start-up fail over time due to severe operating conditions. Failure of these motors not only results in production losses but also high maintenance and repair costs. For this reason, it is very important to detect faults in the LSPMSM starting quickly. In recent years, online status monitoring has been carried out under the supervision of the operating status of the engines. In this study, Supervisory Control and Data Acquisition (SCADA) based state monitoring automation has been realized for remote control of the working status of the LSPMSM with network residual, collection of current and voltage information of the motor and analysis of this information in order to detect bearing fault. The proposed fault detection is based on the statistical process control method based on the motor current signal information. Arduino Mega is the microcontroller at the centre of the hardware department for the purpose of monitoring the situation. The low-cost selection of the microcontroller used makes it accessible to all, but a low sampling frequency (~ 1000 samples per second) is a disadvantage. Exponentially Weighted Moving Average (EWMA) graphing method was used in order to make fault detection from low frequency sampling values. All the data displayed in the SCADA interface and stored in the database can successfully detect bearing fault through online status monitoring of the LSPMSM with mains start.