• Journal of Institute of Control, Robotics and Systems
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• v.9 no.11
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• pp.874-882
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• 2003

The condition assessment of engineering systems has increased in importance because the manpower needed to operate and supervise various plants has been reduced. Especially, induction motors are at the core of most engineering processes, and there is an indispensable need to monitor their health and performance. So detection and diagnosis of motor faults is a base to improve efficiency of the industrial plant. In this paper, a model-based fault detection system is developed for induction motors, using steady state vibration signals. Early various fault detection systems using vibration signals are a trivial method and those methods are prone to have missed fault or false alarms. The suggested motor fault detection system was developed using a model-based reference value. The stationary signal had been extracted from the non-stationary signal using a data segmentation method. The signal processing method applied in this research is FFT. A reference model with spectra signal is developed and then the residuals of the vibration signal are generated. The ratio of RMS values of vibration residuals is proposed as a fault indicator for detecting faults. The developed fault detection system is tested on 800 hp motor and it is shown to be effective for detecting faults in the air-gap eccentricities and broken rotor bars. The suggested system is shown to be effective for reducing missed faults and false alarms. Moreover, the suggested system has advantages in the automation of fault detection algorithms in a random signal system, and the reference model is not complicated.

• Journal of the Korean Society of Industry Convergence
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• v.14 no.3
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• pp.95-100
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• 2011

The modern society longing for the convenience of up-to-date technology, there are attempts of miniaturization and high reliance of power equipments in the effectiveness aspect of urban area's usage of space while requiring more electrical energy than now. Consequently, paper used to the Neral Network for a forcasting conservation system. A neral network is powerful asta modeling tool that is able to capture and represent complex input/output relationships. The true power and advantage of neral networks lies in their ability to learn these relationships directly from the data being modeled. Traditional linear models are simply inadequate when it comes to modeling data that contains non-linear characteristics. Form results of this study, the Neral Network is will play an important role for insulation diagnosis system of real site GIS and power eqipment using $SF_6$ gas.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.11a
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• pp.43-48
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• 2008

Gear system has been widely used in industrial applications and unexpected failures of gears are not only extremely damaging but also lead to economic losses. So, early detection of fault is important for diagnosis machine condition. And acoustic emission is an efficient non destructive testing technique for the diagnosis of machine health and is useful technique for early detection of fault because it can find low-amplitude and high-frequency signal on account of high sensibility. Therefore, in this paper, the AE signal was measured and preprocessed using envelop analysis for gearbox with misalignment between pinion and gear. And then the vibration characteristic of gear misalignment was analyzed.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.346-353
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• 2013

Industrial processes need to be monitored in real-time based on the input-output data observed during their operation. Abnormalities in an induction motor should be detected early in order to avoid costly breakdowns. To early identify induction motor faults, this paper effectively estimates spectral envelopes of each induction motor fault by utilizing a linear prediction coding (LPC) analysis technique and an expectation maximization (EM) algorithm. Moreover, this paper classifies induction motor faults into their corresponding categories by calculating Mahalanobis distance using the estimated spectral envelopes and finding the minimum distance. Experimental results shows that the proposed approach yields higher classification accuracies than the state-of-the-art approach for both noiseless and noisy environments for identifying the induction motor faults.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.12
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• pp.1243-1249
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• 2008

Gear system has been widely used in industrial applications and unexpected failures of gears are not only extremely damaging but also leading to economic losses. So, early detection of fault is important for diagnosis machine condition. And acoustic emission is an efficient non-destructive testing technique fur the diagnosis of machine health and is useful technique far early detection of fault because it can find low-amplitude and high-frequency signal on account of high sensibility. Therefore, in this paper, the AE signal was measured and preprocessed using envelope analysis for gearbox with misalignment between pinion and gear. And then the gear misalignment's vibration characteristic were analyzed.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.57-64
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• 2024

In the industrial field, robots are used to increase productivity by replacing labors with dangerous, difficult, and hard tasks. However, failures of individual industrial robots in the entire production process may cause product defects or malfunctions, and may cause dangerous disasters in the case of manufacturing parts used in automobiles and aircrafts. Although requirements for early diagnosis of industrial robot failures are steadily increasing, there are many limitations in early detection. This paper introduces methods for diagnosing robot failures using sound-based data and deep learning. This paper also analyzes, compares, and evaluates the performance of failure diagnosis using various deep learning technologies. Furthermore, in order to improve the performance of the fault diagnosis system using deep learning technology, we propose a method to increase the accuracy of fault diagnosis based on an inference window. When adopting the inference window of deep learning, the accuracy of the failure diagnosis was increased up to 94%.

• The Journal of the Petrological Society of Korea
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• v.25 no.3
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• pp.211-230
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• 2016

Detailed mapping along the Keumwang fault reveals a complex history of multiple brittle reactivations following late Jurassic and early Cretaceous ductile shearing. The fault core consists of a 10~50 m thick fault gouge layer bounded by a 30~100 m thick damaged zone. The Pre-cambrian gneiss and Jurassic granite underwent at least six distinct stages of fault movements based on deformation environment, time and mechanism. Each stage characterized by fault kinematics and dynamics at different deformation environment. Stage 1 generated mylonite series along the Keumwang shear zone by sinistral ductile shearing during late Jurassic and early Cretaceous. Stage 2 was a mostly brittle event generating cataclasite series superimposed on the mylonite series of the Keumwang shear zone. The roundness of pophyroclastes and the amount of matrix increase from host rocks to ultracataclasite indicating stronger cataclastic flow toward the fault core. At stage 3, fault gouge layer superimposed on the cataclasite generated during stage 2 and the sedimentary basins (Umsung and Pungam) formed along the fault by sinistral strike-slip movement. Fragments of older cataclasite suspended in the fault gouge suggest extensive reworking of fault rocks at brittle deformation environments. At stage 4, systematic en-echelon folds, joints and faults were formed in the sedimentary basins by sinistral strike-slip reactivation of the Keumwang fault. Most of the shearing is accommodated by slip along foliations and on discrete shear surfaces, while shear deformation tends to be relatively uniformly distributed within the fault damage zone developed in the mudrocks in the sedimentary basins. Fine-grained andesitic rocks intruded during stage 4. Stage 5 dextral strike-slip activity produced shear planes and bands in the andesitic rocks. ESR(Electron Spin Resonance) dates of fault gouge show temporal clustering within active period and migrating along the strike of the Keumwang fault during the stage 6 at the Quaternary period.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.10a
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• pp.83-83
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• 1998

• Economic and Environmental Geology
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• v.42 no.6
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• pp.635-643
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• 2009

In this paper, a Quaternary fault is described, which is developed in the mid-eastern part of Ulsan Fault Zone, near the southern Gaegok-ri, Oedong-eub, Gyeongju, Korea. The Gaegok 6 fault is developed along the contact between Early Tertiary granite and Quaternary gravel deposit overlying unconformably the granite. The fault strikes $N02^{\circ}{\sim}22^{\circ}E$ and dips $45^{\circ}{\sim}80^{\circ}$ to the west. This fault has a 30~50 cm wide cataclastic shear zone with gouge zone, mixed with Quaternary sediments and fault breccia of granite. In the main Quaternary fault plane, the orientation of striation is $17^{\circ}$, $356^{\circ}$, indicating a dextral strike-slip faulting with some normal component. There is another striation ($78^{\circ}$, $278^{\circ}$ and $43^{\circ}$, $270^{\circ}$) with reverse-slip sense, developed on the subsidiary plane which cuts the main Quaternary fault plane. In brief, the fault has been developed between the granite in the western part and the Quaternary gravel deposit in the eastern part. The western block of fault is uplifted. The striations and movement senses of faults indicate multiple compressional stages in this region. The fault has a similar orientation, westward dipping geometric pattern, and reverse sensed kinematic pattern with Gaegok 1 fault developed in the north. Thus, the Gaegok 6 fault is probably a southern extension of Gaegok 1 fault.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.2
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• pp.258-266
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• 2012

Recently, Acoustic Emission (AE) technique is widely applied to develop the early fault detection system, and the problem about a signal processing method for AE signal is mainly focused on. In the signal processing method, envelope analysis is a useful method to evaluate the rolling element bearing problems and Wavelet transform is a powerful method to detect faults occurred on gearboxes. However, exact method for AE signal is not developed yet. Therefore, in this paper, two methods, which is Hilbert transforms (HT) and Hilbert-Huang transforms (HHT), will be compared for development a signal processing method for early fault detection system by using AE. AE signals were measured through a fatigue test. HHT has better advantages than HT because HHT can show the time-frequency domain result. But, HHT needs long time to process a signal, which has a lot of data, and has a disadvantage in de-noising filter.