| Summary: | Induction motors are becoming crucial components in numerous industries. The daily usage of induction motors creates the demand for proper maintenance and slight fault detection to avoid serious damage to the induction motor and the shutdown of industries. Among the various kinds of faults in induction motors, bearing faults, broken rotor bar faults, and short-circuit insulation faults are the most common. Thus, detection and classification of these faults in initial stage are attracting great attention. There are conventional methods for detecting such faults, such as the vibration method for bearing faults, the self-organizing map in the case of broken rotor bar faults, and motor current signature analysis for short-circuit insulation faults. From an industrial point of view, diagnosis methods that can classify all these major faults are required. However, reports on the detection and classification of these faults in initial stage using common diagnosis methods are scarce. In this paper, all three kinds of notable faults in an induction motor were artificially induced, and diagnoses using motor stator current spectral features and the rotation speed of the motor were performed. The diagnosis was accomplished using an auto-tunable and arbitrary featured support vector machine algorithm. Although the faults were minor, a high accuracy rate was obtained. The capability to classify the faults and the high diagnosis accuracy prove the robustness and high sensitivity of the method, enabling its practical applications in industries.
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