• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.8
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• pp.131-137
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• 2005

The purpose of this paper is to evaluate the axial magnetic flux measurement could be used as a tool of the condition monitoring system for the induction motor and to develope the diagnostic algerian for the electric motors. The magnetic leakage flux signal is captured by the flux coil located at the end of motor without the disturbance of the operation. And the signal is analyzed both time and frequency bases to detect the failure of the motor. Specific signature can be described in time and frequency domain for each faults of the motor. The spectrum of the signal was found more useful for the monitoring purpose. The supply voltage imbalance and tin to turn failure of the stator winding could be detected by analysing the specific sidebands of the axial flux and sideband of the rotor bar pass frequency with the high resolution spectrum. The goal of this study verity that the axial flux measurement for the induction motor is a powerful tool for the diagnostic method and develope the algorithm to detect the fault.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.638_639
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• 2009

The motors faults including mechanical rotor imbalances, broken rotor bar, bearing failure and eccentricities problems are reflected in electric, electromagnetic and mechanical quantities. This paper presents a study and the practical implementation of an induction motor for reactor containment fan cooler in nuclear power plant with Electric Signature Analysis(ESA). The results obtained present a good degree of reliability hence; the ESA predictive maintenance tools enable a pro-active evaluation of induction motors performance prior to failure.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.243-245
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• 2005

Condition monitoring is desirable for increasing machinery availability, reducing consequential damage, and improving operational efficiency. In this paper, a model-based method using neural network modeling of induction noter in vibration spectra is proposed for machine fault detection and diagnosis. The short-time Fourier transform (STFT) is used to process the quasi-steady vibration signals to continuous spectra so that the neural network model can be trained with vibration spectra. And the faults are detected from changes in the expectation of vibration spectra modeling error. The effectiveness of the proposed method is demonstrated through experimental results.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2008.05a
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• pp.277-280
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• 2008

In this work, the development of the diagnosis algorithm is carried out for identifying health and faulted conditions in three-phase induction motors. The algorithm consists of feature calculation, feature extraction, and feature classification procedures in sequence. Signals for this algorithm are acquired by current and flux sensors simultaneously, the latter is to measure the change of magnetic flux at the air-gap, This work proposes the efficient diagnosis method for induction motors by developing the powerful algorithm. The calculated features show a good linearity according to faults severities. Moreover. the final results show a good classification rate on motor conditions.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.2
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• pp.160-163
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• 2008

In this paper, an auto-diagnosis method of the stator winding fault for small induction motor is suggested. 3-phase stator currents are sampled, filtered, and transformed with Park's vector transformation. After then Park's vector patterns are obtained. To detect the stator winding fault automatically, a distortion ratio is newly defined and compared with the one of healthy motor, and the threshold levels of distortion ratio are suggested. The 2-turn, 4-turn, 8-turn winding fault are tested with no load, 25%, 50%, 75%, and 100% rated load. The distortion ratio of the Park's vector patterns are increased as the increase of the faulted turns, but are same as the increase of the load.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.689-691
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• 1995

In this paper, a fault detection and isolation unit(FDIU) for a centrifugal pump system driven by DC-motor is proposed. The proposed scheme can be classified into the dedicated observer scheme(DOS). A fuzzy logic based inference engine is adopted for the isolation of each faults. Having the fuzzy inference engine, the proposed FDIU resolve a few important problems of the conventional DOSs with conventional two valued logic. The ouputs of the proposed FDIU are not "ith fault occurred" but the grade of memberships that indicate the consistency of observered symptoms(residuals) with each fault symptoms stored in the rule base. The ouputs can easily be transferred to the ranking of the fault possibilities and it will provide very useful informations in monitoring the process. The simulation results show that the FDIU has very good diagnostic ability even in the noisy environment.

• Journal of Power Electronics
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• v.10 no.6
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• pp.702-708
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• 2010

In this paper, a fault diagnosis algorithm for brushless DC (BLDC) motor drives is proposed to maintain control performance under switch open-damage. The proposed fault diagnosis algorithm consists of a simple algorithm using measured phase current information and it detects open-circuit faults based on the operating characteristic of BLDC motors. The proposed algorithm quickly recovers control performance due to its short detection time and its reconfiguration of the system topology. It can be embedded into existing BLDC drive software as a subroutine without additional sensors. The feasibility of the proposed fault diagnosis algorithm is proven by simulation and experimental results.

• Journal of Power Electronics
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• v.13 no.6
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• pp.985-990
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• 2013

This paper proposes a novel fault diagnosis scheme using parameter estimation of the stator resistance, especially in the case of the open-phase faults of PMSM drives. The stator resistance of PMSMs can be estimated by the recursive least square (RLS) algorithm in real time. Fault diagnosis is achieved by analyzing the estimated stator resistance of each phase according to the fault condition. The proposed fault diagnosis scheme is implemented without any extra devices. Moreover, the estimated parameter information can be used to improve the control performance. The feasibility of the proposed fault diagnosis scheme is verified by simulation and experimental results.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.969-970
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• 2007

In this thesis, an algorithm of fault detection and diagnosis during operation for induction motors under the condition of various loads and rates is investigated. For this purpose, the spectrum pattern of input cutterrents was used to monitor the state of induction motors, and by clustering the spectrum pattern of input currents, the newly occurrence of spectrums pattern caused by faults were detected. For diagnosis of the fault detected, the fuzzy fault tree was designed, and the fuzzy relation equation representing the relation between an induction motor fault and each fault type, was solved. The solution of the fuzzy relation equation shows the possibility of each fault's occurring.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.358-360
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• 2007

In this paper, an auto-diagnosis method of the stator winding fault for small induction motor is suggested. 3-phase stator currents are sampled, filtered, and transformed with Park's vector transformation. After then Park's vector patterns are obtained. To detect the stator winding fault automatically, a distortion ratio (id/iq) is newly defined and compared with the one of healthy motor, and the threshold levels are suggested. The 2-turn, 4-turn, 8-turn winding fault are tested with no load, 25%, 50%, 75%, and 100% rated load. The distortion ratio of the Park's vector patterns are increased as the increase of the faulted turns, but are same as the increase of the load.