Winding Fault Detection in Motor Using Near Infra-Red Sensor Signal Based Dywt and Tdywt Analysis

Abstract

Introduction: Runtime monitoring of the stator and rotor windings of asynchronous motors is essential for early detection of faults. Early fault detection enables rapid recovery and prevents severe operational failures during critical stages. Rotor winding faults can lead to turn-to-turn faults, which in turn increase fault current, thermal stress, and the temperature of the motor windings. If inter-turn insulation faults are not identified promptly, they can escalate to rotor bending and coil burning. However, continuous surveillance of motor windings and insulation during operation is challenging.

Objectives: This study aims to develop a more effective fault detection mechanism for asynchronous motors by addressing the limitations of traditional monitoring methods. The goal is to enhance the accuracy and reliability of fault diagnosis, particularly for rotor winding faults and inter-turn insulation failures, during motor operation.

Methods: The proposed approach utilizes Near-Infrared (NIR) sensor-based analysis in conjunction with signal processing through two different wavelet transform techniques: dyadic and transverse dyadic wavelet transforms. These methods are used to analyse signals collected from the motor in real-time.

Results: The analysis demonstrated a notable improvement in fault detection accuracy compared to traditional diagnostic methods. The use of NIR sensors with dyadic and transverse dyadic wavelet transforms enhanced the sensitivity and effectiveness of runtime monitoring, making it more reliable for early fault identification in asynchronous motor windings.

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