• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.17-24
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• 2014

This paper proposes a fault detection method for low-speed rolling element bearings of an induction motor using acoustic emission signals and histogram modeling. The proposed method performs envelop modeling of the histogram of normalized fault signals. It then extracts and selects significant features of each fault using partial autocorrelation coefficients and distance evaluation technique, respectively. Finally, using the extracted features as inputs, the support vector regression (SVR) classifies bearing's inner, outer, and roller faults. To obtain optimal classification performance, we evaluate the proposed method with varying an adjustable parameter of the Gaussian radial basis function of SVR from 0.01 to 1.0 and the number of features from 2 to 150. Experimental results show that the proposed fault identification method using 0.64-0.65 of the adjustable parameter and 75 features achieves 91% in classification performance and outperforms conventional fault diagnosis methods as well.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.2
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• pp.102-108
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• 2007

The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. This study introduces a technique to detect and identify faults in induction motors. Stator currents were measured and stored by time domain. The time domain is not suitable for representing current signals, so wavelet transform is used to convert the signal; onto frequency domain. The raw signals can not show the significant feature, therefore difference values are applied. The difference values were transformed by wavelet transform and the features are extracted from the transformed signals. The dynamic time warping method was used to identify the four fault types. This study describes the results of detecting fault using wavelet analysis.

• Proceedings of the KIEE Conference
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• 2009.04a
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• pp.77-79
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• 2009

Nonlinear system fault detection and isolation of this paper is about the failure of unknown function approximation using neural network for fault detection and isolation techniques of induction motors were applied. observer-based fault signal residual value was used. Induction motor using the speed controller of the backstepping controller. Proposed fault detection and isolation to prove the performance of the simulation was applied to and the actual system.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1079-1080
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• 2011

This paper deals with the operating characteristics of induction motors with broken rotor bar, stator winding inter-turn short and their complex fault conditions. The considered operating characteristics are phase current, torque and speed. Since the operating characteristics of induction motors are directly related to their slip conditions, this paper built the experimental set to adjust the speed of induction motor with a permanent magnet synchronous generator connected to a load bank. From the various experimental results, it is shown that the faults do not highly affect on the operating characteristics of induction motors in low slip conditions, but the fault characteristics can be easily found in larger slip conditions.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.3
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• pp.178-182
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• 2016

FIDVR(Fault Induced Delayed Voltage Recovery) is a phenomenon that recovery of the system voltage level delays after the fault. Cause of FIDVR phenomenon is motor load characteristic about voltage and reactive power. In low voltage condition, the motor go to stall state that consume large amount of reactive power. As a result, the voltage recovery problem is that of repeated occurrences of sustained low voltage following faults on the system. In this paper, analysis the characteristics of the motor load. And using the korean power system actual data, perform a case studies to voltage delay recovery phenomenon alleviation method. Change of each parameters by analyzing the effect on system and selecting an influence parameter. In addition, dynamic characteristic analysis of the resulting difference in the proportion by the motor load in power systems, considering the effect on the voltage stability.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.6
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• pp.55-62
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• 2006

For the fault diagnosis of three-phase induction motors, we construct a experimental unit and then develop a diagnosis algorithm based on pattern recognition. The experimental unit consists of machinery module for induction motor drive and data acquisition module to obtain the fault signal. As the first step for diagnosis procedure, preprocessing is performed to make the acquired current simplified and normalized. To simplify the data, three-phase current is transformed into the magnitude of Concordia vector. As the next step, feature extraction is performed by kernel principal component analysis(KPCA) and linear discriminant analysis(LDA). Finally, we used the classifier based on radial basis function(RBF) network. To show the effectiveness, the proposed diagnostic system has been intensively tested with the various data acquired under different electrical and mechanical faults with varying load.

• Proceedings of the KIPE Conference
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• 2007.07a
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• pp.506-508
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• 2007

In this paper, the induction motor bearing fault diagnosis system using current signals which are measured by over-sampling method is presented. In the case of inverter fed motor drive unlike line-driven motor drive, that make a lot of noise which can cause a wrong fault signals because of PWM(pulse width modulation) voltage. So, the current signals for fault diagnosis need very precise and high resolution information, which means this system demand additional hardware such as low pass filter, high resolution ADC system and so on to use fault diagnosis system. Therefore, the proposed over-sampling method is expected to contribute to low cost fault diagnosis system even though previous inverter fed motor drive without any additional hardware. In order to confirm the presented algorithms, various experiments for bearing faults are tested and the line current spectrum of each faulty situation using park transformation is compared with a FFT results.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.5
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• pp.23-30
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• 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 KIPE Conference
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• 2005.07a
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• pp.643-646
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• 2005

The paper discusses the main problems in induction motor diagnosis by motor current and vibration signals, possible faults and effects produced by these faults in the signal spectrums. Decision Tree is introduced as a tool to diagnose the motor status, this expert system is implemented to detect the incipient defects, supervise and predict them, and plan the maintenance of the motor.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.60-67
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• 2018

The presence of induction motor loads in a power system may cause the phenomenon of delayed voltage recovery after the occurrence of a severe fault. A high proportion of induction motor loads in the power system can be a significant influence on the voltage stability of the system. This problem referred to as FIDVR(Fault Induced Delayed Voltage Recovery) is commonly caused by stall of small HVAC unit(Heating, Ventilation, and Air Conditioner) after transmission or distribution system failure. This delayed voltage recovery arises from the dynamic characteristics associated with the kinetic energy of the induction motor load. This paper proposes the UVLS (Under Voltage Load Shedding) control strategy for dealing with FIDVR. UVLS based schemes prevent voltage instability by shedding the load and can help avoid major economic losses due to wide-ranging cascading outages. This paper review recent topic about under voltage load shedding and compare decentralized load shedding scheme with conventional load shedding scheme. The load shedding strategy is applied to an actual system in order to verify the proposed FIDVR mitigation solution. Simulations demonstrate the effectiveness of the proposed method in resolving the problem of delayed voltage recovery in the Korean Power System.