• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.37-44
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• 2014

This paper proposes a new diagnosis algorithm to detect broken rotor bars (BRBs) faults in induction motors. The proposed algorithm is composed of a frequency signal dimension order (FSDO) estimator and a fault decision module. The FSDO estimator finds a number of fault-related frequencies in the stator current signature. In the fault decision module, the fault diagnostic index from the FSDO estimator is used depending on the load conditions of the induction motors. Experimental results obtained in a 75 kW three-phase squirrel-cage induction motor show that the proposed diagnosis algorithm is capable of detecting BRB faults with an accuracy that is superior to a zoom multiple signal classification (ZMUSIC) and a zoom estimation of signal parameters via rotational invariance techniques (ZESPRIT).

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.07a
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• pp.518-521
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• 2003

A new measurement system was developed by fault diagnosis system for traction motor using current signal analysis. The motor current signature analysis method is used for traction motor fault diagnosis. The diagnosis system program is constructed by artificial neural networks algorithm, those results from the program are used to train neural networks. The trained neural networks have the ability to compute adaptive results for non-trained inputs, and to calculate very fast due to original parallel structure of neural networks with high accuracy within destined tolerance. This system suggested that available test for checking, the probable extent of aging, and the rate of which aging is taking place.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2222-2224
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• 2002

Induction motors are critical components of many industrial machines and are frequently integrated in commercial equipment. The heavy economical losses and the deterioration of system reliability might be caused by the failure of induction motors in industrial field. Based on the reliability and cost competitiveness of driving system (motors), the faults detection and diagnosis of system is considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method are used for induction motor fault diagnosis. This method analyzes the motors supply current. since this diagnoses faults of the motor. The diagnostic algorithm is based on the artificial neural network, and the diagnosis system is programmed by using LabVIEW and MATLAB.

• Proceedings of the KSR Conference
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• 2003.10c
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• pp.529-534
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• 2003

Within industry induction motors have a broad application area to drive pumps, fans, elevators and electric trains. Sudden failures of such machines can cause the heavy economical losses and the deterioration of system reliability. Based on the reliability and cost competitiveness of driving system (motors), the faults detection and the diagnosis of system are considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method are used for induction motor fault diagnosis. This method analyzes the motor's supply current, since this diagnoses faults of the motor. The diagnostic algorithm is based on the principal component analysis(PCA), and the diagnosis system is programmed by using LabVIEW and MATLAB.

• Journal of Electrical Engineering and Technology
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• v.5 no.1
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• pp.140-145
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• 2010

The monitoring and the diagnosis of the faults in induction motors starting from the stator current are very interesting, since it is an accessible and measurable quantity. The spectral analysis of the stator current makes it possible to highlight the characteristic frequencies of the faults but in a wide frequency range depending on half the sampling frequency, making it very difficult to monitor on-line the faults. In order to facilitate the use of the relevant frequencies of machine faults we proposed the extraction of the frequency components using two methods, namely, the amplitude and the instantaneous frequency. The theoretical bases of these methods were presented and the results were validated on a test bench with an induction motor of 5.5 kw.

• Journal of the Korean Society of Safety
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• v.20 no.3 s.71
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• pp.1-8
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• 2005

The industrial induction motor is widely used in the rotating electrical machine for the transmission of power. It is very reliable equipment, but it could lead to the loss of production and lift when failure occurs. Therefore, the failure data is acquired and analyzed by attaching an exclusive instrument to existing induction motor. However, these instruments could lead to side effects, increasing the production costs, because they are very expensive. The purpose of this study is the development of an induction motor bearing failure diagnosis system constructed using LabVIEW which can be supplied the kernelled function, process monitoring and current signature analysis. In addition, the availability and reasonability of the constructed system was examined for an induction motor with failure defects in outer raceway and ball bearing. From the results, it shows that failure diagnosis system constructed is useful for real-time monitoring with detection of bearing defects over the web.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2172-2174
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• 2001

Induction motors are a critical component of many industrial machines and are frequently integrated in commercial equipment. The many economical losses and the deterioration of system reliability might be caused by the failure of induction motors in industrial field. Based on the reliability and cost competitiveness of driving system (motors), the faults detection and diagnosis of system is considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method is used for induction motor fault diagnosis. This method analyzes the motor's supply current, since this diagnoses the motor's condition. The diagnostic system is constructed by using LabVIEW of National Instruments.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.645-648
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• 2003

Induction motors are a critical component of industrial processes. Sudden failures of such machines can cause the heavy economical losses and the deterioration of system reliability. Based on the reliability and cost competitiveness of driving system (motors), the faults detection and the diagnosis of system are considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method is used for induction motor fault diagnosis. This method analyses the motor's supply current. since this diagnoses faults of the motor. The diagnostic algorithm is based on the principal component analysis(PCA), and the diagnosis system is programmed by using LabVIEW and MATLAB.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.65.1-65
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• 2001

Induction motors are a critical component of many industrial machines and are frequently integrated in commercial equipment. The many economical losses and the deterioration of system reliability might be caused by the failure of induction motors in industrial field. Based on the reliability and cost competitiveness of driving system motors, the faults detection and diagnosis of system is considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis MCSA method are emphasized. In this paper, MCSA method is used for induction motor fault diagnosis. This method analyzes the motor´s supply current, since this diagnoses the motor´s condition. The diagnostic system is constructed by using LabVIEW of National Instruments.

• Corrosion Science and Technology
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• v.7 no.4
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• pp.197-200
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• 2008

In this paper, physical scale modeling was employed to identify the configurations of ICCP system and the electric field signatures. Computational boundary element modeling technique has been used to simulate the performance of the CP system and to predict the associated electric fields signatures. The optimization methods combined with the computer models and physical scale modeling will be presented here, which enable the optimum system design to be achieved both in terms of the location and current output of the anode but also in the location of reference electrodes for impressed current cathodic protection(ICCP) systems. The combined methodology was utilized to determine optimal placement of ICCP components (anodes and reference electrodes) and to evaluate performance of ICCP system for the 2%, 10% and 14% wetted hull coatings loss. The objective is to design the system to minimise the electric field while at the same time provide adequate protection for the ship. The results show that experimental scale modeling and computational modeling techniques can be used in concert to design an optimum ICCP system and to provide information for quickly analysis of the system and its surrounding environment.