• Journal of Electrical Engineering and Technology
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• v.8 no.4
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• pp.938-944
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• 2013

In this paper, we propose a method for suppressing shaft voltage by modifying the shape of the rotor and the permanent magnets in interior permanent magnet-type-high-voltage motors. Shaft voltage, which is induced by parasitic components and the leakage flux in motor-driven systems, adversely affects their bearings. In order to minimize shaft voltage, we designed a magnet rearrangement and rotor re-structuring of the motor. The shaft voltage suppression effect of the designed model was confirmed experimentally and by comparative finite element analysis.

• Journal of the Korean Society of Safety
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• v.25 no.3
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• pp.1-6
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• 2010

Ball bearings are one of main components that support the rotational shaft in high speed rotary machinery. So, it is very important to detect the incipient faults and fault growth of bearing since the damage and failure of bearing can cause a critical failures or accidents of machinery system. In the past, many researchers mainly performed to detect the bearing fault using traditional method such as wavelet, statistics, envelope etc in vibration signals. But study on the detection technique for bearing fault growth has a little been performed. In this paper, we verified the possibility for monitoring of fault growth and detection of fault size in bearing outer-race by using the envelope powerspectrum and probabilistic density function from measured vibration signals.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.12
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• pp.1233-1240
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• 2004

Condition monitoring and fault diagnosis of machines are gaining importance in the industry because of the need to increase reliability and to decrease possible loss of production due to machine breakdown. By comparing the nitration signals of a machine running in normal and faulty conditions, detection of faults like mass unbalance, shaft misalignment and bearing defects is possible. This paper presents a novel approach for applying the fault diagnosis of rotating machinery. To detect multiple faults in rotating machinery, a feature selection method and support vector machine (SVM) based multi-class classifier are constructed and used in the faults diagnosis. The results in experiments prove that fault types can be diagnosed by the above method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.537-543
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• 2003

Condition monitoring and fault diagnosis of machines are gaining importance in the industry because of the need to increase reliability and to decrease possible loss of production due to machine breakdown. By comparing the vibration signals of a machine running in normal and faulty conditions, detection of faults like mass unbalance, shaft misalignment and bearing defects is possible. This paper presents a novel approach for applying the fault diagnosis of rotating machinery. To detect multiple faults in rotating machinery, a feature selection method and support vector machine (SVM) based multi-class classifier are constructed and used in the faults diagnosis. The results in experiments prove that fault types can be diagnosed by the above method.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.78-83
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• 2014

Recently, a rapid growth of wind power system as a leading renewable energy source has compelled a number of companies to develop intelligent monitoring and diagnostic system. Such systems can detect early mechanical faults, which prevents from costly repairs. Generally, fault diagnostic system for wind turbines is based on vibration and process signal analysis. In this work, different type of mechanical faults such as mass unbalance and shaft misalignment which can always happen in wind turbine system is considered. The proposed intelligent fault diagnostic algorithm utilizes artificial neural network and Wavelet transform. In order to verify the feasibility of the proposed algorithm, mechanical fault generation experimental system manufactured by Gaon corporation is utilized.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.9
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• pp.1269-1275
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• 2015

Turbine-generator torsional response is caused by interaction between electrical transient air-gap torque and mechanical characteristics of turbine-generator shafts. There are various factors that affects torsional interaction such as fault, circuit breaker switching and generator mal-synchronizing, etc. Fortunately, we can easily simulate above torsional interaction phenomena by using ElectroMagnetic Transient Program (EMTP). However, conventional EMTP shows the incomplete response of super- synchronous torsional mode since it does not consider turbine blade section. Therefore, in this paper, we introduced mechanical-electrical analogy for detailed modeling of turbine-generator shaft system including low pressure turbine blade section. In addition, we derived the natural frequencies of modeled turbine-generator shaft system including turbine blade section and analyzed the characteristics of mechanical torque response at shaft coupling and turbine blade root area according to power system balanced/unbalanced faults.

• 한국기계연구소 소보
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• s.17
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• pp.61-68
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• 1987

The main objective of this paper is to develop the computer-aided vibrational signal processing and monitoring system of rotating machinery. This system has an automatic data acquisition capability and analyze for machine fault diagnosis. By spectrum analysis, machine’s failure can be identified. The monitoring system enables diagnosis of the fault in rotating machinery. In this study, the conventional electrical fans are selected as a model case. The date processing and fault monitoring system proposed here can be applied to the automation of the inspection process in assembling motor-shaft systems. The automatic inspection can enhance the product quality and keep it stable. Since the proposed system is developed for personal computers, it might be cheap in cost and easy in installation.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.5
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• pp.66-75
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• 1997

A fault diagnosis system for a variable speed rotating shaft probably demands a huge database, which makes it diffcult to be realized. This stuydy presents an effective method of fault diagnosis for variable speed rotating shafts. The proposed method is based upon a fuzzy reasoning and it includes a stepwize strategy to reduce the size of database in a diagnosis system. A computer program is developed to show the procedure of the diagnosis, and four cases of faults are applied to the program to illustarate the effectiveness of the proposed method. The propsed method is found to be useful in reducing the size of database from observation of the data files of the dianosis system. The case studies show that the proposed method can be useful for the diagnosis of variable speed rotating shafts.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.50 no.8
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• pp.416-426
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• 2001

This paper deals with the impact of an inverter station on the torsional dynamics of turbine-generator which is located at the inverter side of a HVDC-AC network power system. The studies show that the torsional stress of turbine-generator depends on the AC network fault locations because of the commutation failures of inverter station. And the torsional stress induce fatigue in the shaft material and reduce the shaft life-time. So, the purpose of this paper is to analysis the torsional stress of turbine-generator shaft at inverter side, to find the checked points of turbine-generator. The EMTDC program is used for the simulation studies.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.6
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• pp.36-45
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• 2005

This paper addresses the application of motor current spectral analysis for the detection of rolling-element bearing damage in induction machines. We set the experimental test bed. They is composed of the normal condition bearing system, the abnormal rolling-element bearing system of 2 type induction motors with shaft deflection system by external force and a hole drilled through the outer race of the shaft end bearing of the four pole test motor. We have developed the embedded distributed fault tolerant and fault diagnosis system for industrial motor. These mechanisms are based on two 32-bit DSPs and each TMS320F2407 DSP module is checking stator current The effects on the stator current spectrum are described and related frequencies are also determined. This is an important result in the formulation of a fault detection scheme that monitors the stator currents. We utilized the FFT(Fast Fourier Transform), Wavelet analysis and averaging signal pattern by inner product tool to analyze stator current components. Especially, the analyzed results by inner product clearly illustrate that the stator signature analysis can be used to identify the presence of a bearing fault.