• Journal of the Korea Society of Computer and Information
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• v.22 no.4
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• pp.1-8
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• 2017

In this paper, we propose a fault isolation system for device drivers of the Linux operating system. High availability systems impose stringent requirements upon Linux operating system. Especially device drivers can be a major source of operating system instability and many times contribute to system degradation and outages. The proposed fault isolation system identifies the occurrence of the memory-related faults in device driver and isolates it from the kernel. By operating at the early stage of the page fault handler in Linux kernel, the system detects which module causes fault and isolates it transparently from the remaining part of the kernel. By experiments, we show that the proposed system efficiently detects faults incurred by device driver, isolates the device driver and the process which accessed the driver module from the kernel.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2006.03a
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• pp.33-40
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• 2006

Since early 1990's, several Quaternary faults have been found in the southeastern part of the Korean peninsula with reference to fault activity. Because some of these faults could be considered a capable fault, it is a very delicate matter, which need to be deal with carefully in assessing the seismic hazard. In determining whether or not a faults are capable, because of the low rate of seismicity and insufficient relationship between instrumental macro-seismicity and fault, there has been considerable debate among geologists and geophysicists in Korea. In this study, we discuss the criteria and probabilistic approaches that are used to assess whether or not a fault is seismogenic. And, we preliminarily also suggest the probability of fault activity from the spatial association between faults and earthquake epicenters, fault slip and tectonic stress, and geological evidence for multiple episodes of reactivation.

• Proceedings of the KIPE Conference
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• 2014.07a
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• pp.522-523
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• 2014

Analysis of faults in induction motors has become a major field of research due to importance of loss and damage reduction and maximum online performance of motors. There are several methods to analyze the faults in an induction motor from conventional Fourier transform to modern decision-making neural networks. Considering detectability of fault among all methods, a new fault detection solution has been proposed; it is called as frequency-domain Discrete Wavelet Transform (FD-DWT). In this method, the stator current is decomposed through series of low- and high-pass filters and consequently, the fault characteristics are more visible, because additional components have been reduced. The objective of this paper is early detection of input voltage unbalance in induction motor using wavelet transform in frequency domain. Experimental results show the effectiveness of the proposed method in early detection of faults.

• Journal of Korean Society for Quality Management
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• v.45 no.1
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• pp.25-38
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• 2017

Purpose: Hot strip rolling mill consists of a lot of mechanical and electrical units. In condition monitoring and diagnosis phase, various units could be failed with unknown reasons. In this study, we propose an effective method to detect early the units with abnormal status to minimize system downtime. Methods: The early warning problem with various units is defined. K-means and PAM algorithm with Euclidean and Manhattan distances were performed to detect the abnormal status. In addition, an performance of the proposed algorithm is investigated by field data analysis. Results: PAM with Manhattan distance(PAM_ManD) showed better results than K-means algorithm with Euclidean distance(K-means_ED). In addition, we could know from multivariate field data analysis that the system reliability of hot strip rolling mill can be increased by detecting early abnormal status. Conclusion: In this paper, clustering-based monitoring and fault detection algorithm using Manhattan distance is proposed. Experiments are performed to study the benefit of the PAM with Manhattan distance against the K-means with Euclidean distance.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.04a
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• pp.664-665
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• 2013

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.1049-1055
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• 2013

DC motors are widely used in industries like cement, paper manufacturing, etc., even today. Early fault identification in dc motors significantly improves its life time and reduces power consumption. Many conventional and soft computing techniques for fault identification in DC motors including a recent work using model based analysis with the help of fuzzy logic are available in literature. In this paper fuzzy logic and norm based wavelet analysis of startup transient current are proposed to identify and quantify the armature winding fault and bearing fault in DC motors, respectively. Results obtained by simulation using Matlab and Simulink are presented in this paper to validate the proposed work.

• Economic and Environmental Geology
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• v.30 no.2
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• pp.143-151
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• 1997

The history of the Palgongsan Fault comprises the growth-fault, the dormant and the strike-slip phases. Dissecting the Palgongsan Granite, the Palgongsan Strike-slip Fault, which is the product of the final phase, sinistrally offset about 5.5 km as shown in the dislocation of the Hasandong Formation. Faulting, sedimentation and igneous activity were inter-related in the early phases of the Palgongsan Fault. Some other faults such as the Dansan Pond Fault and the Hayang Fault have also been discovered, and their some stratigraphic implications and the ages of faulting are discussed. The anomalous development of the Jindong Formation in the study area and the related stratigraphic problems are discussed. It has been confirmed that the Konchonri Formation deposited over the Chaeyaksan Volcanic Formation in spite of the recent doubts on their such stratigraphic relation. The chronological sequence of the volcanisms of the Kyongsang Basin has been summarized.

• Advances in Computational Design
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• v.8 no.3
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• pp.255-271
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• 2023

The gear drive of a combat aircraft engine is responsible for power transmission to the different accessories necessary for the engine's operation. Incorrect power transmission can occur due to the presence of failure modes in the gears like bending fatigue, pitting, adhesive wear, scuffing, abrasive wear and polished wear etc. Fault diagnosis of the gear drive is necessary to get an early indication of failure of the gears. The present research is to develop an algorithm using different vibration signal processing techniques on industrial vibration acquisition systems to establish gear fault diagnosis architecture. The signal processing techniques have been used to extract various feature vectors in the development of the fault diagnosis architecture. An open-source dataset of other gear fault conditions is used to validate the developed architecture. The results is a basis for development of artificial intelligence based expert systems for gear fault diagnosis of a combat aircraft engine.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.1114-1118
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• 2000

Impulsive sound and vibration signals in gear are often associated with their faults. Thus these impulsive sound and vibration signals can be used as indicators in the diagnosis of gear fault. The early detection of impulsive signal due to gear fault prevents from complete failure in gear. However it is often difficult to make objective measurement of impulsive signals because of background noise signals. In order to ease the detection of impulsive signals embedded in background noise, we enhance the impulsive signals using adaptive signal processing.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.11a
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• pp.84-88
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• 2008

The roller bearings play an important role not only sustain radial or axial load of system, but carry out a rotatory movement as a various operating conditions. They happen that incipient faults which were caused by excessive load, manufacturing or assembling process's errors and many other reasons are created. The bearing faults make noise and vibration by a continuous collision of rotatory components, which can lower the quality and stability of auto-transmission. Therefore, it is important to detect the early fault as soon as possible. This paper presents a detecting method for the improvement in quality by developing the program which can be used to analyze and predict the vibrational characteristics caused by roller bearing faults. We completed development of the inspection system of vibration by appling the most efficient detecting methods and verified the system's reliability through experiments.