• Journal of the Korean Society for Precision Engineering
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• v.19 no.3
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• pp.52-58
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• 2002

The purpose of condition monitoring and fault diagnosis is to detect faults occurring in machinery in order to improve the level of safety in plants and reduce operational and maintenance costs. The recognition performance is important not only to gain a high recognition rate bur a1so to minimize the diagnosis failures error rate by using off effective rejection module. We examined the problem of performance evaluation for the rejection scheme considering the accuracy of individual c1asses in order to increase the recognition performance. We use the Smith's method among the previous studies related to rejection method. Nearest neighbor classifier is used for classifying the machine conditions from the vibration signals. The experiment results for the performance evaluation of rejection show the modified optimum rejection method is superior to others.

• Journal of the Korean Society of Safety
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• v.15 no.2
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• pp.1-7
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• 2000

This paper presents the approach involving fault detection and diagnosis of gears using pattern recognition and Wavelet transform. It describes result of the comparison between KDI (Kullback Discrimination Information) with the nearest neighbor classification rule as one of pattern recognition methods and Wavelet transform to know a way to detect and diagnosis of gear damages experimentally. To model the damages 1) Normal (no defect), 2) one tooth is worn out, 3) All teeth faces are worn out 4) One tooth is broken. The vibration sensor was attached on the bearing housing. This produced the total time history data that is 20 pieces of each condition. We chose the standard data and measure distance between standard and tested data. In Wavelet transform analysis method, the time series data of magnitude in specified frequency (rotary and mesh frequency) were earned. As a result, the monitoring system using Wavelet transform method and KDI with nearest neighbor classification rule successfully detected and classified the damages from the experimental data.

• Journal of Advanced Navigation Technology
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• v.3 no.1
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• pp.32-43
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• 1999

In this paper, we designed an intelligent fault detection and isolation algorithm for improving reliability of turbojet engine controller. The proposed method uses multilayer neural network to detect and accommodate sensor failure from the functional relationship of dissimilar sensors. Signals of failure sensors are estimated from neural network trained by backpropagation algorithm. Simulation results on the state-space model of a turbojet engine illustrate that the proposed algorithm achieves the desired performance.

• Proceedings of the KIEE Conference
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• 2002.11b
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• pp.137-140
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• 2002

This paper describes the effect of distributed generations (DG) on the bus protection scheme. When the generating capacity of DG is larger than 3 MVA totally, DG are generally connected to the 22.9 kV bus directly with the dedicated line. Due to the fault current contribution of DG, the overcurrent protective relay that have conventional set value cannot detect the fault occurred in distribution power network with DG. Therefore. the impacts from interconnection of DG on the overcurrent protective relay for bus protection should be accurately assessed and mitigated. Simulation results show that it would be necessary to modify the overcurrent protective relay set value for protecting the bus according to the generating capacity of DG.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.388-393
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• 2012

Vibration condition monitoring at low rotational speeds is still a challenge. Acoustic emission (AE) is the most used technique when dealing with low speed bearings. At low rotational speeds, the energy induced from surface contact between raceway and rolling elements is very weak and sometimes buried by interference frequencies. This kind of issue is difficult to solve using vibration monitoring. Therefore some researchers utilize artificial damage on inner race or outer race to simplify the case. This paper presents vibration signal analysis of low speed slewing bearings running at a low rotational speed of 15 rpm. The natural damage data from industrial practice is used. The fault frequencies of bearings are difficult to identify using a power spectrum. Therefore the relatively improved method of empirical mode decomposition (EMD), ensemble EMD (EEMD) is employed. The result is can detect the fault frequencies when the FFT fail to do it.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.1006-1008
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• 1997

This paper presents the logic based High Impedance Fault(HIF) detection rules for distribution lines. Due to the characteristics of HIF, which shows low current on relaying points, it is difficult to detect the fault occurred in distribution line by the conventional overcurrent relay(OCR) and/or harmonics relay. The HIF data were generated by using TACS in EMTP. In this paper, The harmonic index is defined as the ratio of harmonic component to fundamental component. The proposed HIF detection rules are obtained by analysing the difference between normal condition and HIF condition.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.7 s.361
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• pp.112-121
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• 2007

In this paper, we propose a new checkpointing strategy for dual modular redundancy real-time systems. For every checkpoints the execution results from two processors, and the result saved in the previous checkpoint are compared to detect faults. We devised an operation algorithm in chectpoints to recover from transient faults as well as permanent faults. We also develop a Markov model for the optimization of the proposed checkpointing strategy. The probability of successful task execution within its deadline is derived from the Markov model. The optimal number of checkpoints is the checkpoints which makes the successful probability maximum.

• Journal of the Korean Society of Safety
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• v.31 no.3
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• pp.28-33
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• 2016

Since defects in the wheels of railway vehicles, which occur due to wears with the rail, cause serious damage to the running device, the diagnostic monitoring system for condition-based maintenance is required to secure the driving safety. In this paper, we studied to apply a useful Cepstrum analysis to detect periodic structure in spectrum among the vibration signal processing techniques for the fault diagnosis of a rotating body such as wheel. In order to analyze in variations of train velocity, the Cepstrum analysis was performed after a domain change of the vibration signal from time domain to rotation angle domain. When domains change, it is important to use a interpolation for a uniform interval of the rotation angle. Finally, the Cepstrum analysis for wheel flat detection was verified by using the vibration signal including the disturbance resulting from the rail irregularities and the vibration of bogie components.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.1
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• pp.23-26
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• 2005

This paper describes the algorithm for deciding the status of the operating machines in the power plants. It is very important to decide whether the status of the operating machines is good or not in the industry to protect the accidents of machines and improve the operation efficiency of the plants. There are two steps to analyze the status of the running machines. First, we extract the features from the input original data. Second, we classify those features into normal/abnormal condition of the machines using the wavelet transform and the input RMS vector through the K-means algorithm. In this paper we developed the algorithm to detect the fault operation using the K-means method from the sound of the operating machines.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.1
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• pp.57-62
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• 2019

In this paper, oneM2M based data monitoring and acquisition system is designed and implemented to measure and transmit the voltage, current, temperature, acceleration and vibration of the motor. The proposed system can detect electrical faults (overcurrent, reverse phase, phase loss, ground fault) and mechanical faults (MC counter, motor operation time, bearing and winding temperature, motor speed, insulation resistance). The system consists of sensor data collection, web server, php, database, wired/wireless communication system. The insulation resistance and the motor speed were measured, and the experimental results were similar for both the test resistance value and the reference input value.