• 전기학회논문지
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• 제60권9호
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• pp.1783-1787
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• 2011

The slip frequency is included in feature frequency for fault diagnosis of rotor bar, so rotating rotor speed is needed. In this study, rotor slot harmonic(RSH) method is suggested for speed estimation of induction motor. When the rotor is rotating, motor current signal include the harmonic signal of back-emf voltage related with number of rotor slot. So from the power spectrum of current signal, the rotor speed can be founded. This method of rotor speed estimation gives the slip frequency, and the feature frequency of rotor bar fault can be calculated. Comparing with stroboscope speed meter, the error rate of suggested method is less than 0.1[%].

• Journal of Magnetics
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• 제21권3호
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• pp.413-420
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• 2016

The permanent magnet synchronous motor has high efficiency driving performance and high power density output characteristics compared with other motors. In addition, it has good regenerative operation characteristics during braking and deceleration driving condition. For this reason, permanent magnet synchronous motor is generally applied as a power train motor for electrical vehicle. In permanent magnet synchronous motor, the most probable causes of fault are demagnetization of rotor's permanent magnet and short of stator winding turn. Therefore, the demagnetization fault of permanent magnet and turn fault of stator winding should be detected quickly to reduce the risk of accident and to prevent the progress of breakdown of power train system. In this paper, the fault diagnosis method using high frequency low voltage injection was suggested to diagnose the demagnetization fault of rotor permanent magnet and the turn fault of stator winding. The proposed fault diagnosis method can be used to check the faults of permanent magnet synchronous motor during system check-up process at vehicle starting and idling stop mode. The feasibility and usefulness of the proposed method were verified by the finite element analysis.

• 조명전기설비학회논문지
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• 제21권3호
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• pp.27-34
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• 2007

농형유도전동기에서 회전자 도체의 고장은 전동기 성능에 나쁜 영향을 미친다. 본 논문에서는 이와 같은 고장유무를 수치해석적 방법으로 분석하는 방법을 제안하였다. 정상이 아닌 회전자 도체가 있는 농형유도전동기의 회전자에서 고장에 따라 나타나는 특징적인 파형을 수치 해석적 방법으로 분석하였다. 이와 같은 방법으로 정상적인 도체와 고장 도체가 있는 경우의 특징을 분석함으로써 고장의 유무, 상태를 파악할 수 있다. 회전자 고장진단 실험 장치를 제작하고 실험을 통하여 제안한 방법의 타당성과 유용성을 확인하였다.

• 전기학회논문지
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• 제59권10호
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• pp.1882-1888
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• 2010

It is widely known that ESA(Electric Signature Analysis) method is very useful one for fault diagnosis of an induction motor. Online fault diagnosis system of induction motors using LabVIEW is proposed to detect the fault of broken rotor bars and shorted turns in stator. This system is not model-based system of induction motor but LabVIEW-based fault diagnosis system using FFT spectrum of stator current in faulty motor without estimating of motor parameters. FFT of stator current in faulty induction motor is measured and compared with various reference fault data in data base to diagnose the fault. This paper is focused on to predict and diagnose of the health state of induction motors in steady state. Also, it can be given to motor operator and maintenance team in order to enhance an availability and maintainability of induction motors. Experimental results are demonstrated that the proposed system is very useful to diagnose the fault and to implement the predictive maintenance of induction motors.

• 전기학회논문지
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• 제57권12호
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• pp.2173-2178
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• 2008

A doubly-fed induction generator(DFIG) system has been widely used in the modem wind turbines due to variable-speed operation, high efficiency and small converter size. It is well known that an inter-turn fault of a generator is very difficult to be detected. The DFIG system uses a wound rotor induction machine so that the magnetizing current of the generator can be fed from both the stator and the rotor. This paper proposes a protection algorithm for a DFIG using the d-q equivalent circuit in the time domain. In the case of a DFIG, the voltages and currents of the rotor side as well as the voltages and currents of the stator are available. The proposed algorithm estimates the instantaneous(i.e., converted into the stationary frame) induced voltages from the rotor and the stator sides. If the difference between the two estimated induced voltages exceeds the threshold, the proposed algorithm detects the inter-turn fault. The algorithm can detect a inter-turn fault of a winding. The performance of the proposed algorithm is validated using a PSCAD/EMTDC simulator under inter-turn fault conditions and normal operating conditions such as an external fault and the change of the wind speed.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2000년도 추계학술대회논문집
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• pp.802-808
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• 2000

This paper characterizes the electromechanical parameters due to the fault of rotor bars in a squirrel cage induction motor. Simulation is performed to investigate how broken rotor bars have effect on them by solving the time-stepping finite element equation coupled with magnetic field equation, circuit equation and mechanical equation of motion. It shows that the asymmetry of magnetic flux due to the broken rotor bar introduces the beating phenomenon in time domain and the sideband frequencies in frequency spectra, respectively, to the stator current, torque, speed, magnetic force and vibration of a rotor. However, vibration of a rotor would be the most effective monitoring parameters to detect the faults of rotor bars.

• Journal of Power Electronics
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• 제17권4호
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• pp.972-982
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• 2017

The Park's vector of stator current is a popular technique for the detection of induction motor faults. While the detection of the faulty condition using the Park's vector technique is easy, the classification of different types of faults is intricate. This problem is overcome by the Multiple Park's Vector (MPV) approach proposed in this paper. In this technique, the characteristic fault frequency component (CFFC) of stator winding faults, rotor winding faults, unbalanced voltage and bearing faults are extracted from three phase stator currents. Due to constructional asymmetry, under the healthy condition these characteristic fault frequency components are unbalanced. In order to balanced them, a correction factor is added to the characteristic fault frequency components of three phase stator currents. Therefore, the Park's vector pattern under the healthy condition is circular in shape. This pattern is considered as a reference pattern under the healthy condition. According to the fault condition, the amplitude and phase of characteristic faults frequency components changes. Thus, the pattern of the Park's vector changes. By monitoring the variation in multiple Park's vector patterns, the type of fault and its severity level is identified. In the proposed technique, the diagnosis of faults is immune to the effects of unbalanced voltage and multiple faults. This technique is verified on a 7.5 hp three phase wound rotor induction motor (WRIM). The experimental analysis is verified by simulation results.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제55권11호
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• pp.572-576
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• 2006

As the increasing of capacity and technology of power facilities, rotating machines such as turbine generators and water turbines are getting higher at capacity but smaller in size. Thus the monitoring and diagnosis of generators for fault detection and protection has attracted intensive interest. Most of electrical faults of rotating machines appear in their windings. In case of an after-fault in high capacity rotating machines, the recovering cost is usually very expensive and additional time is necessary for returning in a normal situation. In this paper, the magnetic flux patterns in air-gap of a generator under various fault states as well as a normal state are simulated by a conventional FEM tool. These results are successfully applied to detection and diagnosis of the short-circuit condition in rotor windings of a high capacity generator.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2012년도 추계학술대회 논문집
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• pp.400-401
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• 2012

• 한국유체기계학회 논문집
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• 제7권2호
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• pp.41-49
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• 2004

Condition monitoring is very important in turbo machinery because single failure could cause critical damages to its plant. So, automatic fault recognition has been one of the main research topics in condition monitoring area. We have used a relatively new fault recognition method, Hidden Markov Model(HMM), for mechanical system. It has been widely used in speech recognition, however, its application to fault recognition of mechanical signal has been very limited despite its good potential. In this paper, discrete HMM(DHMM) was used to recognize the faults of rotor system to study its fault recognition ability. We set up a rotor kit under unbalance and oil whirl conditions and sampled vibration signals of two failure conditions. DHMMS of each failure condition were trained using sampled signals. Next, we changed the setup and the rotating speed of the rotor kit. We sampled vibration signals and each DHMM was applied to these sampled data. It was found that DHMMs trained by data of one rotating speed have shown good fault recognition ability in spite of lack of training data, but DHMMs trained by data of four different rotating speeds have shown better robustness.