• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2004년도 학술대회 논문집
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• pp.371-374
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• 2004

Induction motors are a critical component of many industrial processes and are frequently integrated in commercially available equipment. Safety, reliability, efficiency, and performance are some of the major concerns of induction motor applications. Preventive maintenance of induction motors has been a topic great interest to industry because of their wide range application of industry. Since the use of mechanical sensors, such as vibration probes, strain gauges, and accelerometers is often impractical, the motor current signature analysis (MACA) techniques have gained murk popularity as diagnostic tool. Fault tolerant control (FTC) strives to make the system stable and retain acceptable performance under the system faults. All present FTC method can be classified into two groups. The first group is based on fault detection and diagnostics (FDD). The second group is independent of FDD and includes methods such as integrity control, reliable stabilization and simultaneous stabilization. This paper presents the fundamental FDD-based FTC methods, which are capable of on-line detection and diagnose of the induction motors. Therefore, our group has developed the embedded distributed fault tolerant and fault diagnosis system for industrial motor. This paper presents its architecture. These mechanisms are based on two 32-bit DSPs and each TMS320F2407 DSP module is checking stator current, voltage, temperatures, vibration and speed of the motor. The DSPs share information from each sensor or DSP through DPRAM with hardware implemented semaphore. And it communicates the motor status through field bus (CAN, RS485). From the designed system, we get primitive sensors data for the case of normal condition and two abnormal conditions of 3 phase induction motor control system is implemented. This paper is the first step to drive multi-motors with serial communication which can satisfy the real time operation using CAN protocol.

• Tuberculosis and Respiratory Diseases
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• 제71권1호
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• pp.30-36
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• 2011

Background: Pneumonia is commonly seen in outpatient clinics. it is widely known as the most common cause of death from infectious disease. Pneumonia has been diagnosed by its typical symptoms, chest X-ray and blood tests. However, both chest X-rays and blood tests have limitations in diagnosis. Thus primary care clinicians usually have been constrained due to a lack of adequate diagnostic tools. Vibration response imaging (VRI) is a newly emerging diagnostic modality, and its procedure is non-invasive, radiation-free, and easy to handle. This study was designed to evaluate the diagnostic usefulness of the VRI test among pneumonia patients and to consider its correlation with other conventional tests such as Chest X-ray, laboratory tests and clinical symptoms. Methods: VRI was performed in 46 patients diagnosed with pneumonia in Konkuk University Medical Center. VRI was assessed in a private and quiet room twice: before and after the treatment. Sensors for VRI were placed on a patient's back at regular intervals; they detected pulmonary vibration energy produced when respiration occurred and presented as specific images. Any modifications either in chest X-ray, C-reactive protein (CRP), white blood cell count (WBC) or body temperature were compared with changes in VRI image during a given time course. Results: VRI, chest X-ray and CRP scores were significantly improved after treatment. Correlation between VRI and other tests was not clearly indicated among all patients. But relatively severe pneumonia patients showed correlations between VRI and chest X-ray, as well as between VRI and CRP. Conclusion: This study demonstrates that VRI can be safely applied to patients with pneumonia.

• 소음진동
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• 제6권3호
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• pp.341-347
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• 1996

진동해석을 이용한 기계계통의 진단에는 시간영역(time domain)에서의 해석 과 FFT(Fast Fourier Transform)를 이용한 주파수영역(frequency domain)에서의 해석 을 생각할 수 있다. 이중 FFT 방법은 고속연산기의 출현과 물리적인 이해의 편이성 등오로 인하여 널리 사용되고 있으나, 시간 함수인 비정상 상태신호(nonstationary signal)의 경우는 주파수영역 해석만으로는 물리적 이해를 구하는데는 한계가 있다. 그래서, 최근 신호처리기법 분야에서는 주파수영역 해석과 시간영역 해석을 보완적 으로 표현할 수 있는 기간-주파수영역 해석기법에 많은 연구가 진행되고 있다. 그중 대표적인 신호처리 기법은 Wigner-Ville Distribution이며, 특히 본 Wigner-Ville Distribution은 많은 물리적 의미를 갖고 있어 주요 연구 대상이며, 많은 응용분야 를 갖고 있다. 그러나, 기계계통중 회전체의 진동신호을 분석하여 고장 진단 및 감시를 용이하기 위해서는 새로운 형태의 시간.주파수영역 해석기법이 필요하다. 본 논문에서는 회전체의 진동신호 분석이 용이하도록 물리적인 의미와 응용상에 중점을 둔 방향서 Wigner-Ville 분포라는 시간-주파수 분석기법을 제안하였고, 회전체를 이용한 실험을 실시하였다. 그 결과 제안된 방향성 Wigner-Ville Distribution은 회전체 진동신호를 시간-주파수 영역에서 잘 표현하고 있으며, 특히 회전체의 수직 및 수평방향 진동신호로 부터 얻어지는 방향성 Wigner-Ville Distribution은 각 주파수 성분의 방향성 정보를 갖고 있어 이를 회전체의 고장 진단 및 감시에 이용 하였다.

• 대한기관식도과학회:학술대회논문집
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• 대한기관식도과학회 1993년도 제27차 학술대회 초록집
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• pp.72-72
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• 1993

음성질환의 진단을 위하여 사용하는 검사법은 여러가지가 있으며 음성발생의 기전에 근거하여 공기역학적 검사로부터 어음청취검사에 이르기까지 다양하게 시도되고 있다. 이중 성대점막의 진동양상은 간접후두경 만으로는 정확히 관찰하기 어려우므로 후두스트로보스코피, 초고속촬영법, 광전, 전기, 초음파등을 이용한 글로토그라피 및 카이모그라피 등이 사용되고 있는데 임상적으로는 후두스트로보스코피가 가장 널리 사용되어지고 있다. 저자들은 1992년 4월 부터 1993년 3월까지 연세대학교 의과대학 음성언어의학연구소에서 음성검사를 시행하였던 환자들을 대상으로 질환별 스트로보스코피소견의 특징을 파악함으로써 후두질환의 진단 및 치료에 도움을 얻고자 하였다.

• Nuclear Engineering and Technology
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• 제55권3호
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• pp.827-838
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• 2023

Centrifugal pump is a key part of nuclear power plant systems, and its health status is critical to the safety and reliability of nuclear power plants. Therefore, fault diagnosis is required for centrifugal pump. Traditional fault diagnosis methods have difficulty extracting fault features from nonlinear and non-stationary signals, resulting in low diagnostic accuracy. In this paper, a new fault diagnosis method is proposed based on the improved particle swarm optimization (IPSO) algorithm-based variational modal decomposition (VMD) and relevance vector machine (RVM). Firstly, a simulation test bench for rotor faults is built, in which vibration displacement signals of the rotor are also collected by eddy current sensors. Then, the improved particle swarm algorithm is used to optimize the VMD to achieve adaptive decomposition of vibration displacement signals. Meanwhile, a screening criterion based on the minimum Kullback-Leibler (K-L) divergence value is established to extract the primary intrinsic modal function (IMF) component. Eventually, the factors are obtained from the primary IMF component to form a fault feature vector, and fault patterns are recognized using the RVM model. The results show that the extraction of the fault information and fault diagnosis classification have been improved, and the average accuracy could reach 97.87%.

• Steel and Composite Structures
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• 제29권6호
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• pp.703-716
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• 2018

Rapid detection of damages in civil engineering structures, in order to assess their possible disorders and as a result produce competent decision making, are crucial to ensure their health and ultimately enhance the level of public safety. In traditional intelligent health monitoring methods, the features are manually extracted depending on prior knowledge and diagnostic expertise. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed here for intelligent health monitoring of civil engineering structures. In the first stage, $Nystr{\ddot{o}}m$ method is used for automatic feature extraction from structural vibration signals. In the second stage, Moving Kernel Principal Component Analysis (MKPCA) is employed to classify the health conditions based on the extracted features. In this paper, KPCA has been implemented in a new form as Moving KPCA for effectively segmenting large data and for determining the changes, as data are continuously collected. Numerical results revealed that the proposed health monitoring system has a satisfactory performance for detecting the damage scenarios of a three-story frame aluminum structure. Furthermore, the enhanced version of KPCA methods exhibited a significant improvement in sensitivity, accuracy, and effectiveness over conventional methods.

• 대한기계학회논문집A
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• 제32권7호
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• pp.556-560
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• 2008

As higher reliability and accuracy on production facilities are required to detect incipient faults, a diagnostic system for predictive maintenance of the facility is highly recommended. In this paper, we present a study on the application of vibration signals to diagnose faults for a Rotating Machinery using the Mahalanobis Distance-Taguchi System. RMS, Crest Factor and Kurtosis that is known as the Statistical Methods and the spectrum analysis are used to diagnose faults as parameters of Mahalanobis distance.

• 대한치과교정학회지
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• 제54권5호
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• pp.325-341
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• 2024

Objective: To correlate temporomandibular joint (TMJ) morphology and position with cone-beam computed tomography (CBCT) images, Joint Vibration Analysis (JVA), and Jaw Tracker (JT) to develop a radiation-free, dynamic method for screening and monitoring the TMJ in orthodontic patients. Methods: A total of 236 orthodontic patients without symptoms of TMJ disorders who had undergone CBCT were selected for the JVA and JT tests in this cross-sectional study. TMJ position and morphology were measured using a three-dimensional analysis software. JT measurements involved six opening-closing cycles, and JVA measurements were performed using a metronome to guide the mouth opening-closing movements of the patients. The correlations among the three measuring devices were evaluated. Results: Abnormalities in condylar surface morphology affected the mandibular range of motion. The cut-off value results show that when various measurement groups are within a certain range, abnormalities may be observed in morphology (area under the curve, 0.81; P < 0.001). A 300/< 300 Hz ratio ≥ 0.09 suggested abnormal morphology (P < 0.05). Correlations were observed among the maximum opening velocity, maximum vertical opening position, and joint spaces in the JT measurements. Correlations were also observed between the > 300/< 300 Hz ratio, median frequency, total integral, integral < 300 Hz, and peak frequency with joint spaces in the JVA measurements. Conclusions: JT and JVA may serve as rapid, non-invasive, and radiation-free dynamic diagnostic tools for monitoring and screening TMJ abnormalities before and during orthodontic treatment.

• 문화기술의 융합
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• 제10권4호
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• pp.663-668
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• 2024

본 논문에서는 웨이퍼 이송 로봇의 고장 진단에 시간 영역에서의 통계적 분석 방법을 적용하고, 진동 및 토크 신호의 중요 특성을 파악하는 방법을 제안한다. 이를 기반으로 데이터의 차원을 축소하기 위해 주성분 분석을 사용하고, 유클리드 거리와 Hotelling의 T-제곱 통계량을 활용하여 고장 진단 알고리즘을 개발했다. 이 알고리즘은 관측된 데이터에 대해 고장 상태를 분류하는 결정 경계를 형성한다. 속도 파라미터를 고려한 데이터 분류는 진단 정확도를 향상시킴을 확인했다. 이러한 접근 방식은 고장 진단의 정확성과 효율성을 개선하는 데 기여한다.

• 대한기계학회논문집A
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• 제38권2호
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• pp.205-210
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• 2014

기계시스템의 결함을 진단하기 위한 방법으로 패턴인식 기법이 널리 사용되고 있다. 진동신호의 변화를 감지하여 기계시스템의 건전성을 판단하는 방법이 패턴인식 기법이다. 대표적 패턴 인식기법으로 최근 은닉 마르코프 모델과 인공신경망이 여러 분야에서 사용되고 있다. 본 연구에서는 결함진단에 은닉 마르코프 모델과 인공신경망을 혼합한 방법이 제시되었으며 결함진단 대상 구조물로는 크랙을 가진 회전하는 풍력터빈 블레이드가 선정되었다. 본 연구에서는 크랙발생 여부뿐만 아니라 그 위치 및 크기도 동시에 진단하고자 하였다. 아울러서 본 연구에서는 일정 주파수들을 갖는 모멘트를 대상 구조물에 가함으로써 외부 잡음에도 불구하고 높은 결함진단 확률을 가질 수 있도록 하였다.