• Journal of Electrical Engineering and Technology
• /
• v.2 no.4
• /
• pp.485-493
• /
• 2007

This paper deals with the problem of detection of induction motor incipient faults. Artificial Neural Network (ANN) approach is applied to detect two types of incipient faults (1). Interturn insulation and (2) Bearing wear faults in single-phase induction motor. The experimental data for five measurable parameters (motor intake current, rotor speed, winding temperature, bearing temperature and the noise) is generated in the laboratory on specially designed single-phase induction motor. Initially, the performance is tested with two inputs i.e. motor intake current and rotor speed, later the remaining three input parameters (winding temperature, bearing temperature and the noise) were added sequentially. Depending upon input parameters, the four ANN based fault detectors are developed. The training and testing results of these detectors are illustrated. It is found that the fault detection accuracy is improved with the addition of input parameters.

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

• The Transactions of the Korean Institute of Electrical Engineers B
• /
• v.54 no.5
• /
• pp.231-237
• /
• 2005

Many conventional multi-level inverters have detected switching faults by using the over voltage and current. However, fault detection of the switching elements is very difficult because the voltage and current due to each switching fault decrease more than the normal operation. Moreover, the dc-link unbalancing voltage causes a serious problem in the safety and reliability of system when the 3-level inverter faults occur Therefore, this paper proposes the simple fault diagnose method and the neutral-point-voltage control method that can protect the 3-level inverter system from the unbalancing voltage of the do-link capacitors when the faults of switching elements occur in the 3-level inverter that is very efficient in ac motor drives of the high voltage and high power applications. Through experiment results, the validity of the proposed method is demonstrated.

• KIEE International Transactions on Power Engineering
• /
• v.2A no.4
• /
• pp.153-159
• /
• 2002

Power transformer protective relay should block the tripping during magnetizing inrush and rapidly operate the tripping during internal faults. Recently, the frequency environment of power system has been made more complicated and the quantity of 2nd frequency component in inrush state has been decreased because of the improvement of core steel. And then, traditional approaches will likely be maloperate in the case of magnetizing inrush with low second harmonic component and internal faults with high second harmonic component. This paper proposes a new relaying algorithm to enhance the fault detection sensitivities of conventional techniques by using a fuzzy logic approach. The proposed fuzzy based relaying algorithm consists of flux-differential current derivative curve, harmonic restraint, and percentage differential characteristic curve. The proposed relaying was tested with relaying signals obtained from EMTP simulation package and showed a fast and accurate trip operation.

• Proceedings of the KIEE Conference
• /
• 2002.07d
• /
• pp.2222-2224
• /
• 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 KIEE Conference
• /
• 1997.07c
• /
• pp.897-899
• /
• 1997

The analysis of distribution line faults is essential to the proper protections of the power system. A high impedance fault does not make enough current to cause conventional protective devices. It is well known that undesirable operating conditions and certain types of faults on electric distribution feeders cannot be detected by using conventional protection system. This paper describes an algorithm using back-propagation neural network for pattern recognition and detection of high impedance faults. Fractal dimensions are estimated for distinction between random noise and chaotic behavior in the power system. The fractal dimension of the line current is also used as a indication of the high impedance fault.

• Proceedings of the KSR Conference
• /
• 2003.10c
• /
• pp.529-534
• /
• 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 the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.28 no.5
• /
• pp.23-30
• /
• 2014

This paper presents an on-line diagnosis system for identifying health and faulted conditions in squirrel-cage induction motors using stator current, temperature, and partial discharge signals. The proposed diagnosis system can diagnose induction motor faults such as broken rotor bars, air-gap eccentricities, stator winding insulations, and bearing faults. Experimental results obtained from induction motors show that the proposed system is capable of detecting induction motor faults.

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

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.27 no.6
• /
• pp.82-87
• /
• 2013

Electrical leakage happens when faults in electrical apparatus or power lines occur. It causes electrical fires and electric shocks. In order to reduce accidents caused by electrical leakage current, it is important to detect faults effectively and shut off the power. In this paper, firstly we analyzed statistics of electrical fires and electric shocks caused by electrical leakage current. Secondly, standards of allowable leakage current and body impedance models were analyzed. Lastly, effective application methods for breaking electrical leakage current were suggested. The results will provide useful preventive measures of electrical fires and electric shocks caused by electrical leakage.