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RISS 인기검색어
- 검색키워드
- 저자 : Gani M. Ismail (검색결과 1 건)
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Gani M. Ismail,Jothi Swaroopan N. M.,Shanker N. R. 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.3
In Electric motors, identifi cation of insulation and winding faults in stator and rotor during running state is a challenging task. Winding and insulation fault is identifi ed through burning smell of coil, evaluating the effi ciency of motor, or dismantling of motor. Motor running with winding and insulation faults lead to coil-to-coil and phase-to-phase short circuit fault. Winding insulation and winding coil fault in motor leads to unbalanced and diff erential fl ux radiation. Monitoring the winding and insulation during running state of motor is a challenging task. In this paper, monitoring of stator and rotor winding is proposed through NIR sensor during running state of motor. Near Infra-Red (NIR) sensor is fi xed in air gaps of motor. NIR refl ect rays from winding fl ux through air gaps are analysed for faults in stator and rotor winding and insulation. NRI refl ected signals process with spectral band separation and NIR refl ected residual (NRR) signals are obtained. NRR signal process with Tunable Q Wavelet Transform (TQWT) for monitoring and detecting, the insulation and winding fault of motor. Motor allowed to operate at diff erent induced faults such as no load, loaded, stator, rotor insulation fault and stator, rotor-winding fault and NRR signal obtained. Q-factor base Energy band of NRR signals are analysed for winding and insulation faults through sub band energy variations. The low and high frequency component of faulty NRR signal detect with TQWT more accurately. The performance of NIR sensor-based winding and insulation fault diagnosis is compared with conventional transducers such as current signatures and radar signals. The NIR sensor based NRR signals classifi es insulation and winding fault accurately of about 92% compared to current signal signatures.
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