• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.9-17
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• 2023

In this paper, we propose a one-class vibration anomaly detection system for bearing defect diagnosis. In order to reduce the economic and time loss caused by bearing failure, an accurate defect diagnosis system is essential, and deep learning-based defect diagnosis systems are widely studied to solve the problem. However, it is difficult to obtain abnormal data in the actual data collection environment for deep learning learning, which causes data bias. Therefore, a one-class classification method using only normal data is used. As a general method, the characteristics of vibration data are extracted by learning the compression and restoration process through AutoEncoder. Anomaly detection is performed by learning a one-class classifier with the extracted features. However, this method cannot efficiently extract the characteristics of the vibration data because it does not consider the frequency characteristics of the vibration data. To solve this problem, we propose an AutoEncoder model that considers the frequency characteristics of vibration data. As for classification performance, accuracy 0.910, precision 1.0, recall 0.820, and f1-score 0.901 were obtained. The network design considering the vibration characteristics confirmed better performance than existing methods.

• Journal of IKEEE
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• v.25 no.3
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• pp.548-555
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• 2021

This paper outlines the basic concepts, approaches and research trends of fault diagnosis and fault tolerant control applied to industrial processes, facilities, and motor drives. The main role of fault diagnosis for industrial processes is to create effective indicators to determine the defect status of the process and then take appropriate measures against failures or hazadous accidents. The technologies of fault detection and diagnosis have been developed to determine whether a process has a trend or pattern, or whether a particular process variable is functioning normally. Firstly, data-driven based and model-based techniques were described. Secondly, fault detection and diagnosis techniques for industrial processes are described. Thirdly, passive and active fault tolerant control techniques are considered. Finally, major faults occurring in AC motor drives were listed, described their characteristics and fault diagnosis and fault tolerant control techniques are outlined for this purpose.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.3 no.3
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• pp.11-16
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• 2004

In this study, the characteristics of signals such as acoustic emission, vibration amplitude and noise level which are derived from the abnormal condition of compressor are investigated. The normal condition, vane stick sound and roller defect condition are chosen to analyze the signal in each cases. From the feature extraction of each signals, the dominant parameters of each signals which can identify the abnormal condition are suggested.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.2_1
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• pp.177-184
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• 2022

The automatic defect sorting function of machinery parts is being introduced to the automation of the manufacturing process. In the final stage of automation of the manufacturing process, it is necessary to apply computer vision rather than human visual judgment to determine whether there is a defect. In this paper, we introduce a deep learning method to improve the classification performance of typical mechanical parts, such as welding parts, galvanized round plugs, and electro galvanized nuts, based on the results of experiments. In the case of poor welding, the method to further increase the depth of layer of the basic deep learning model was effective, and in the case of a circular plug, the surrounding data outside the defective target area affected it, so it could be solved through an appropriate pre-processing technique. Finally, in the case of a nut plated with zinc, since it receives data from multiple cameras due to its three-dimensional structure, it is greatly affected by lighting and has a problem in that it also affects the background image. To solve this problem, methods such as two-dimensional connectivity were applied in the object segmentation preprocessing process. Although the experiments suggested that the proposed methods are effective, most of the provided good/defective images data sets are relatively small, which may cause a learning balance problem of the deep learning model, so we plan to secure more data in the future.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.11-22
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• 2023

A radiating elements fault diagnosis method with simplified radiation pattern measurement procedure was presented for planar active phased array antenna system. For presenting the mentioned method, the technique for linear approximation based on the radiation characteristics of a planar array configuration and a technique for solving a unique solution problem that occur in process of diagnosing a fault in a radiating elements were presented. Based on the presented method and a genetic algorithm, experimental simulations were performed for radiating element defect diagnosis according to various planar active phased array antenna configurations. As a result, it was confirmed that the presented radiating element fault diagnosis method can be smoothly applied to planar active phased antennas having various configurations.

• Journal of the Korean Society for Nondestructive Testing
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• v.28 no.4
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• pp.323-330
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• 2008

High cycle bending fatigue of socket welded small bore pipe was characterized, and also the fatigue crack initiation of small bore pipe was monitored in situ by the acoustic emission (AE) technique. The STS 316L stainless steel specimens were prepared by gas tungsten arc welding (GTAW) process having the artificial defect (i.e., lack of penetration) and defect free at the root. The fatigue failure was occurred at the loc for high stress and root for relatively low stress. The crack initiation cycles ($N_i$) was defined to the abrupt increase in AE counts during the fatigue test, and then the cracks were observed by the radiographic test and electron microscope before and after the fatigue crack initiation cycles. The socket welded pipe damaged by bending fatigue was studied regarding the welding defect, failure mode, and crack initiation cycles for the diagnosis and monitoring.

• Journal of Korean Foot and Ankle Society
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• v.22 no.2
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• pp.49-54
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• 2018

Ankle sprains are among the most common injuries sustained during athletic activities and daily life. Acute ankle sprain is usually managed conservatively with functional rehabilitation but the failure of conservative treatment leads to the development of chronic ankle instability. The development of repetitive ankle sprains and persistent symptoms after injury has been termed chronic ankle instability. Acute ankle sprains and chronic ankle instability require a careful evaluation to detect other comorbidities, such as subtalar instability, osteochondral defect, peroneal tendinopathy, tarsal coalition, os trigonum, flexor hallucis longus tendinitis, calcaneus anterior process fracture, and neural injuries. For the successful treatment of acute ankle sprains and chronic ankle instability, the treatment of comorbidity lesions should be performed first.

• Journal of the Korean Society of Safety
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• v.18 no.2
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• pp.6-15
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• 2003

A method is presented for diagnosing the converter bearings by using acoustic emission. The flaking mechanism causing the large-scale bearing for furnace to flaw is investigated and a possibility of defect is verified by Finite Element method. he diagnosis logic is proposed fir detecting the flaw of a non-continuous rotating machine. It is proved that the acoustic emission energy can be used as a representative parameter for an acoustic event. Applying the method to the tilting bearings for steel mill in operation, the effectiveness of this logic is evaluated. It is shown that AE signal is generated only when the bearing is tilting, and the trend analysis can be focused upon this process.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.48 no.4
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• pp.247-252
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• 1999

In this paper, in order to investigate availability of the OW-PD measurement method which has been proposed as an alternative of AC-PD measurement method to an after laying test and/or diagnosis for the power cable system, partial discharges owing to the needle-type defect integrated into the cable have been measured using AC and OW(Oscillating Wave) voltages. In the AC-PD measurement, the magnitude, phase and pulse number of partial discharges have been changed with the duration of voltage application, which can be analyzed through the relation with the process of the electrical tree initiation and propagation. In addition, the characteristics of partial discharges using OW voltage are appeared to be similar to those in case of AC-PD measurement and to be different with the shapes of electrical tree. From these results, it is concluded that the OW-PD measurement method is available to the tests for the cable system.

• Smart Structures and Systems
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• v.21 no.5
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• pp.687-694
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• 2018

The problem of wheel tread defects has become a major challenge for the health management of high-speed rail as a wheel defect with small radius deviation may suffice to give rise to severe damage on both the train bogie components and the track structure when a train runs at high speeds. It is thus highly desirable to detect the defects soon after their occurrences and then conduct wheel turning for the defective wheelsets. Online wheel condition monitoring using wheel impact load detector (WILD) can be an effective solution, since it can assess the wheel condition and detect potential defects during train passage. This study aims to develop an FBG-based track-side wheel condition monitoring method for the detection of wheel tread defects. The track-side sensing system uses two FBG strain gauge arrays mounted on the rail foot, measuring the dynamic strains of the paired rails excited by passing wheelsets. Each FBG array has a length of about 3 m, slightly longer than the wheel circumference to ensure a full coverage for the detection of any potential defect on the tread. A defect detection algorithm is developed for using the online-monitored rail responses to identify the potential wheel tread defects. This algorithm consists of three steps: 1) strain data pre-processing by using a data smoothing technique to remove the trends; 2) diagnosis of novel responses by outlier analysis for the normalized data; and 3) local defect identification by a refined analysis on the novel responses extracted in Step 2. To verify the proposed method, a field test was conducted using a test train incorporating defective wheels. The train ran at different speeds on an instrumented track with the purpose of wheel condition monitoring. By using the proposed method to process the monitoring data, all the defects were identified and the results agreed well with those from the static inspection of the wheelsets in the depot. A comparison is also drawn for the detection accuracy under different running speeds of the test train, and the results show that the proposed method can achieve a satisfactory accuracy in wheel defect detection when the train runs at a speed higher than 30 kph. Some minor defects with a depth of 0.05 mm~0.06 mm are also successfully detected.