• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.6 no.2
• /
• pp.294-299
• /
• 2002

This paper presents a faults diagnosis technique of induction motors based on a neural network. Only stator current is measured, transformed by using FFT and normalized for the training. Healthy, bearing fault, stator fault and rotor end-ring fault motors are prepared to obtain the learning data and diagnose the several faults. For more effective diagnosis, the load rate is changed by 100%, 60%, 30% of full load and the obtained are applied to the teaming process. The experimental results show the proposed method is very detectable and applicable to the real diagnosis system.

• Journal of Mechanical Science and Technology
• /
• v.20 no.7
• /
• pp.929-940
• /
• 2006

Early detection and diagnosis of incipient induction machine faults increases machinery availability, reduces consequential damage, and improves operational efficiency. However, fault detection using analytical methods is not always possible because it requires perfect knowledge of a process model. This paper proposes a neural network based expert system for diagnosing problems with induction motors using vibration analysis. The short-time Fourier transform (STFT) is used to process the quasi-steady vibration signals, and the neural network is trained and tested using the vibration spectra. The efficiency of the developed neural network expert system is evaluated. The results show that a neural network expert system can be developed based on vibration measurements acquired on-line from the machine.

• Proceedings of the KIEE Conference
• /
• 1998.07c
• /
• pp.952-954
• /
• 1998

This paper presents an artificial neural network approach to diagnose and detect faults in oil-filled power transformers based on dissolved gas analysis. The proposed algorithm is used to detect faults with or without cellulose involved. Several neural network topologies have been considered. Good diagnosis accuracy is obtained with the proposed approach.

• Proceedings of the KIEE Conference
• /
• 1996.07b
• /
• pp.1353-1355
• /
• 1996

This paper presents classification of high impedance fault pattern using linear prediction coefficients. A feature of neutral phase current is extracted by the linear predictive coding. This feature is classified into faults by a multilayer perceptron neural network. Neural network successfully classifies test data into three faults and one normal state.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.6
• /
• pp.503-510
• /
• 2002

This paper presents a fault diagnosis method using neural network-based multi-fault models and statistical method to detect and isolate faults in nonlinear systems. In the proposed method, faults are detected when the errors between the system output and the neural network nominal system output cross a predetermined threshold. Once a fault in the system is detected, the fault classifier statistically isolates the fault by using the error between each neural network-based fault model output and the system output. From the computer simulation results, it is verified that the proposed fault diagonal method can be performed successfully to detect and isolate faults in a nonlinear system.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.47 no.2
• /
• pp.51-59
• /
• 2010

Two logic transformations, De Morgan and re-substitution, are sufficient to convert a unate gate network (UGN) to a more general balanced inversion parity (BIP) network. Circuit classes of interest are discussed in detail. We prove that De Morgan and re-substitution transformations are test-set preserving for path delay faults. Using the results of this paper, we can easily show that a high-level test set for a function z that detects all path delay faults in any UGN realizing z also detects all path delay faults in any BIP realization of z.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.53 no.3
• /
• pp.161-165
• /
• 2004

In this paper, we have designed the ground faults detection and interrupting controller at normal condition of AC 120v to 240v rating voltage. Ground faults in electrical network have the characteristics of low current, 60㎐ frequency to 2㎑frequency. The load condition are no load and 20A load. The trip level of the controller is 6㎃ with ground faults. The Controller algorithm is implemented using pic16c71 microprocessor.

• International Journal of Air-Conditioning and Refrigeration
• /
• v.11 no.3
• /
• pp.125-131
• /
• 2003

The fault detection and diagnosis technology may be applied in order to decrease the energy consumption and the maintenance cost of the air-conditioning system. In this paper, two fault detection methods were considered. One is a generic neural network, and the other is an moving average neural network. In order to compare the performance of fault detection results from these methods, two different types of faults in an air-conditioning system were applied. These are the condenser 30% fouling and the evaporator fan 25% slowdown. Test results showed that the moving average neural network was more effective for the detection of partial faults in the air-conditioning system.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.67 no.9
• /
• pp.1146-1151
• /
• 2018

Recently, researches on the intelligence of electric power facilities have been trying to apply artificial intelligence techniques as computer platforms have improved. In particular, faults occurring in substation should be able to quickly identify possible faults and minimize power fault recovery time. This paper presents fault location technique for 154kV substation using neural network. We constructed a training matrix based on the operating conditions of the circuit breaker and IED to identify the fault location of each component of the target 154kV substation, such as line, bus, and transformer. After performing the training to identify the fault location by the neural network using Weka software, the performance of fault location discrimination of the designed neural network was confirmed.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.7 no.1
• /
• pp.45-53
• /
• 1997

This paper presents hybrid expert system for diagnosis of electric power transformer faults. The expert system diagnose and detect faults in oil-filled power transformers based on dissolved gas analysis. As the preprocessing stage, fuzzy information theory is used to manage the uncertainty in transformer fault diagnosis using dissolved gas analysis. The Kohonen neural network takes the interim results by applying fuzzy informations theory as inputs, and performs the transformer fault diagnosis. The Proposed system tested gas records of power transformers from Korea Electric Power Corporation to verify the diagnosis performance of transformer faults.