• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.749-752
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• 2014

본 논문에서는 저속으로 동작하는 구름 베어링의 다중 결함 조기 검출을 위해 결함 특징 추출, 효과적인 특징 선택, 선택된 특징을 이용한 결함 분류의 세 단계로 구성된 결함 진단 기법을 제안한다. 1단계에서 이산 웨이블릿 변환을 이용하여 미세성분으로부터 통계적 결함 특징을 추출하고, DET(distance evaluation technique)를 이용하여 추출한 결함 특징 가운데 베어링 다중 결함 검출에 효과적인 특징을 선택한다. 마지막으로 선택된 특징을 k-NN(k-Nearest Neighbors) 분류기 입력으로 사용함으로써 결함을 진단한다. 본 논문에서는 제안한 결함 진단 기법의 성능을 분류 정확도 측면에서 평가한 결과 95.14%의 높은 분류 정확도를 보였다.

• Journal of IKEEE
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• v.27 no.4
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• pp.518-523
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• 2023

The diagnosis of Inter Turn Short Circuits (ITSC) in induction motors is critical due to the escalating severity of faults resulting from even minor disruptions in the stator windings. However, diagnosing ITSC presents significant challenges due to similarities in noise and losses shared with 3-phase induction motors. Although artificial intelligence techniques have been explored for efficient diagnosis, practical applications heavily rely on model-based methods, necessitating further research to enhance diagnostic performance. This study proposed a diagnostic method applied the Clarke Transformation approach, focusing solely on current components while disregarding changes in rotating flux. Experimental results conducted over a 30-minute period, encompassing both normal and ITSC conditions, demonstrate the effectiveness of the proposed approach, with FAR(False Accept Rates) of 0.2% for normal-to-ITSC FRR(False Rejection Rates) and 0.26% for ITSC-to-normal FRR. These findings underscore the efficacy of the proposed approach.

• Journal of the Korean Society of Propulsion Engineers
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• v.5 no.1
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• pp.69-75
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• 2001

Linear GPA(Gas Path Analysis) and non-linear GPA programs for performance diagnostics of a turboprop engine were developed, and a study for selection of optimal measurement variables was performed. Simultaneous faults in the compressor, the compressor turbine and the power turbine, which occur damage of the engine, were assumed. The non-linear GPA analysis was carried out with an iterative method, where the performance degradation rate of independent parameters was divided into same intervals. It was compared with the result by the Newton-Raphson method for observing the effect of an iterative method. According to the analysis result, it was found that performance of non-linear GPA can be influenced on the type of the iterative method. For showing effects of the number of measurement variables both the linear and non-linear GPAs were analyzed with 10, 8 and 6 measurement sets, respectively. RMS error between them were compared each other. It was realized that the more measurement parameters are used, and the more accurate result may be obtained. However much better result can be obtained with measurement parameters selected properly Moreover, RMS error by using non-linear GPA was less than that by using linear GPA.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.511-516
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• 2024

The recently enacted detailed guidelines on the performance evaluation of track facilities presented the necessary requirements regarding the evaluation procedures and implementation methods of track performance evaluation. However, the grade of rail surface damage is determined by external inspection (visual inspection), and there is no choice but to rely only on qualitative evaluation based on the subjective judgment of the inspector. Therefore, in this study, we attempted to develop a diagnostic application that can diagnose rail internal defects using rail surface damage. In the field investigation, rail surface damage was investigated and patterns were analyzed. Additionally, in the indoor test, SEM testing was used to construct image data of rail internal damage, and crack length, depth, and angle were quantified. In this study, a deep learning model (Fast R-CNN) using image data constructed from field surveys and indoor tests was applied to the application. A rail surface damage diagnosis application (App) using a deep learning model that can be used on smart devices was developed. We developed a smart diagnosis system for rail surface damage that can be used in future track diagnosis and performance evaluation work.

• Journal of Korean Tunnelling and Underground Space Association
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• v.17 no.5
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• pp.553-561
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• 2015

In this study, the performance assessment criterion for reasonable maintenance of shield TBM tunnel was presented. The performance assessment items such as crack, leakage, breakage, spalling, exfoliation/detachment, efflorescence, quality condition, exposure of steel, carbonation, faulting step, bolts condition, drainage condition, ground condition, contact section condition and conduit condition were selected by analyzing domestic and foreign performance assessment criterions and investigating segment lining deterioration cases through the site investigation and in-depth inspection analysis result on the shield TBM tunnel. In addition, the reasonable weight using AHP (Analytic Hierarchy Process) were estimated.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.477-480
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• 2004

최근 생산 현장에 최종 제품의 성능 보장을 위해 사용될 소재의 특성을 검사하는 장비가 도입.운영되고 있다. 이들 장치 중 Rheotruder는 폴리머 소재의 품질 평가기준이 되는 점도를 측정하기 위해 제작되었으며 이는 지연시간 및 비선형적 특성을 갖게 되어 시스템의 분석이 용이하지 않다는 문제점을 갖는다. 본 연구에서는 비선형 특성을 갖는 측정 장치의 성능 평가를 용이하게 하기 위해 동적 시스템 모델링이 가능한 TDNN(Time Delay Neural Network)을 도입하여 실제 Rheotruder에 적용하여 봄으로써 그 유용성을 확인하고자 한다.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.10a
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• pp.133-137
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• 1999

STAREX 교환기의 DBMS는 교환기 시스템의 호처리, 운용, 보전 등에 관련된 모든 데이터를 유지하면서, 응용 프로그램 데이터의 효율적 지원, 데이터의 일관성 유지, 데이터의 백업, 데이터의 무결성 진단 및 복구 등의 종합적인 관리를 수행하는 시스템 소프트웨어이다. 또한, DBMS는 교환기의 실시간 처리 요구를 만족시키기 위하여 여러 가지 기능들을 제공한다. 그러나, 이러한 기능들은 응용 프로그램들이 얼마나 효율적으로 사용 하느냐에 따라 성능이 크게 좌우된다. 본 논문에서는 STAREX 교환기의 DBMS가 제공하는 실시간 처리 기능들을 소개하고, 교환기의 성능을 향상시키기 위하여 응용 프로그램들이 효율적으로 데이터베이스에 접근하는 방안을 제시한다.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.27 no.5
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• pp.553-560
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• 2003

On-site diagnosis of chiller performance is an essential step fur energy saving business. The main purpose of the on-site diagnosis is to predict the COP of a target chiller. Many models based on thermodynamics background have been proposed for this purpose. However, they have to be modified from chiller to chiller and require deep insight into thermodynamics that most of field engineers are often lacking in. This study focuses on developing an easy-to-use diagnostic technique that is based on adaptive neuro-fuzzy inference system (ANFIS). Quality of the training data for ANFIS, sampled over June through September, is assessed by checking COP prediction errors. The architecture of the ANFIS, its error bounds, and collection of training data are described in detail.

• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.558-563
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• 2021

알츠하이머병 치매와 조현병 진단을 위한 2단계 분류 모델을 제안한다. 정상군과 환자군의 발화에 나타난 페어 언어 모델 간의 Perplexity 차이에 기반한 분류와 기존 단일 BERT 모델의 미세조정(fine-tuning)을 이용한 분류의 통합을 시도하였다. Perplexity 기반의 분류 성능이 알츠하이머병, 조현병 모두 우수한 결과를 보임을 확인 하였고, 조현병 분류 모델의 성능이 소폭 증가하였다. 향후 설명 가능한 인공지능 기법을 적용에 따른 성능 향상을 기대할 수 있었다.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1088-1097
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• 2021

Vibration data of mechanical equipment inevitably have noise. This noise adversely af ects the maintenance of mechanical equipment. Accordingly, the performance of a learning model depends on how effectively the noise of the data is removed. In this study, the noise of the data was removed using the Denoising Auto Encoder (DAE) technique which does not include the characteristic extraction process in preprocessing time series data. In addition, the performance was compared with that of the Wavelet Transform, which is widely used for machine signal processing. The performance comparison was conducted by calculating the failure detection rate. For a more accurate comparison, a classification performance evaluation criterion, the F-1 Score, was calculated. Failure data were detected using the One-Class SVM technique. The performance comparison, revealed that the DAE technique performed better than the Wavelet Transform technique in terms of failure diagnosis and error rate.