Abstract:

Induction motors are one of the important motor types used in industry. Although these motors are generally of robust construction, they are subject to failures due to ambient operating conditions. The traditional diagnostic methods are based on measuring signals such as current, vibration, temperature, and speed from an experimental setup for good and faulty motors. But finding an equivalent motor that can compare with the motor used in the industry is always difficult. Therefore, by constructing a digital twin of the real motor, signals belonging to the healthy motor can be obtained, which is equivalent to the motor in the industry. In this study, motor stator faults were tried to be diagnosed using digital twin and motor signals obtained from a real experimental setup. The faulty frequency region is determined in the spectrum by estimating the parameters related to the motor current, and the faults are determined according to the information entropy. The operation of the proposed system has been tested with data from both the digital twin and the real motor, and successful results have been obtained.

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