• The Journal of Korea CHUNA Manual Medicine
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• v.1 no.1
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• pp.139-143
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• 2000

통증감소를 위주로 하는 기존의 슬 골관절염 치료는 슬관절의 퇴행을 가속화시킬 수 있기 때문에 한의학적 각종 침구요법 및 물리요법, 약물요법이나 의학적 주사요법 등을 '슬관절에 대한 부하 힘을 감소시킨 상태'에서 시행하는 진료형식을 제안한다. 그리고 이러한 슬관절질환 치료의 추나요법을 채택하여 임상적용이 가능하도록 이를 기계화 도구화하고, 진단과 치료를 동시에 시행할 수 있는 방식이 되도록 연구할 필요가 있다.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.4
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• pp.329-336
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• 2008

The facilities with new technologies in the recent power plants become larger and need a lot of high cost for maintenance due to stop operations and accidents from the operating machines. Therefore, it claims new systems to monitor the operating status and predict the faults of the machines. This research classifies the normal/abnormal status of the machines into 5 levels which are normal-level/abnormal-level/care-level/dangerous-level/fault and develops the new system that predicts faults without stop operation in power plants. We propose the regional segmentation technique in the frequency domain from the data of the operating machines and generate the Peak-codes similar to the Bar-codes, The high efficient and economic operations of the power plants will be achieved by carrying out the pre-maintenance at the care level of 5 levels in the plants. In order to be utilized easily at power plants, we developed the algorithm appling to a notebook computer from signal acquisition to the discrimination.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2007.11a
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• pp.345-349
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• 2007

In this study a fuzzy trend monitoring method for detecting the engine mechanical faults was proposed through analyzing performance trends of measurement data. The trend monitoring is an engine conditioning method which can find engine faults by monitoring important measuring parameters such as fuel flow, exhaust gas temperatures, rotational speeds, vibration. etc. Using engine condition data set as a input which generated by linear regression analysis of real engine instrument data, an application of fuzzy logic in diagnostics estimate a cause of fault in each components.

• The KSFM Journal of Fluid Machinery
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• v.7 no.2 s.23
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• pp.41-49
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• 2004

Condition monitoring is very important in turbo machinery because single failure could cause critical damages to its plant. So, automatic fault recognition has been one of the main research topics in condition monitoring area. We have used a relatively new fault recognition method, Hidden Markov Model(HMM), for mechanical system. It has been widely used in speech recognition, however, its application to fault recognition of mechanical signal has been very limited despite its good potential. In this paper, discrete HMM(DHMM) was used to recognize the faults of rotor system to study its fault recognition ability. We set up a rotor kit under unbalance and oil whirl conditions and sampled vibration signals of two failure conditions. DHMMS of each failure condition were trained using sampled signals. Next, we changed the setup and the rotating speed of the rotor kit. We sampled vibration signals and each DHMM was applied to these sampled data. It was found that DHMMs trained by data of one rotating speed have shown good fault recognition ability in spite of lack of training data, but DHMMs trained by data of four different rotating speeds have shown better robustness.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.4
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• pp.587-595
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• 2002

Recent trend changes a criterion for evaluation of humors that environmental problems are raised as a global issue. Burners with higher thermal efficiency and lower oxygen in the exhaust gas, evaluated better. To comply with environmental regulations, burners must satisfy the NO/sub x/ and CO regulation. Consequently, 'good burner'means one whose thermal efficiency is high under the constraint of NO/sub x/ and CO consistency. To make existing burner satisfy recent criterion, it is highly recommended to develop a feedback control scheme whose output is the consistency of NO/sub x/ and CO. This paper describes the development of a real time flame diagnosis technique that evaluate and diagnose the combustion states, such as consistency of components in exhaust gas, stability of flame in the quantitative sense. In this paper, it was proposed on the flame diagnosis technique of burner using Neuro-Fuzzy algorithm. This study focuses on the relation of the color of the flame and the state of combustion. Neuro-Fuzzy loaming algorithm is used in obtaining the fuzzy membership function and rules. Using the constructed inference algorithm, the amount of NO/sub x/ and CO of the combustion gas was successfully inferred.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.341-344
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• 2004

발전소에서 운전 중인 발전 설비의 장비 및 기계의 동작, 감시, 진단은 매우 중요한 일이다. 발전소의 이상 감지를 위해 상태 모니터링이 사용되며, 이상이 발생되었을 때 고장의 원인을 분석하고 적절한 조치를 계획하기 위한 이상 진단 과정을 따르게 된다. 본 논문에서는 산업 현장에서 기기들의 운전시에 발생하는 기기 발생 음을 획득하여 정상/비정상을 판정하기 위한 알고리듬에 대하여 연구하였다. 사운드 감시(Sound Monitoring) 기술은 관측된 신호를 acoustic event로 분류하는 것과 분류된 이벤트를 정상 또는 비정상으로 구분하는 두 가지 과정으로 진행할 수 있다. 기존의 기술들은 주파수 분석과 패턴 인식의 방법으로 간단하게 적용되어 왔으며, 본 논문에서는 K-means clustering 알고리듬을 이용하여 사운드를 acoustic event로 분류하고 분류된 사운드를 정상 또는 비정상으로 구분하는 알고리듬을 개발하였다.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.633-639
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• 2001

This paper proposes a knowledge-based expert system. which is assembled into hardware organized with sensor module. AID converter, USB. data acquisition PC and software composed of monitoring and diagnosis module combined with a frame-based method using Sohre's chart and a rule-based method. Vibration signals using various sensors are acquired by AID converter. transferred into PC and processed to obtain a continuous monitoring of the machine status displayed into several plots. Through combining frame-base which covers wide vibration causes with rule-base which gives relatively specified diagnosis results, high accuracy of fault diagnosis can be guaranteed and knowledge base can be easily extended by adding new causes or symptoms. Some examples using experimental data show the good feasibility of the proposed algorithm for condition monitoring and diagnosis of industrial rotating machinery.

• Annual Conference of KIPS
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• 2010.04a
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• pp.10-13
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• 2010

저상 굴절 차량에는 전차륜 시스템이 요구되며, AWS ECU는 전차륜 시스템의 핵심적인 역할을 하는 전자 제어 장치이다. 실제 차량 주행을 위해서는 차량에 따른 ECU의 설정 값 변경이 필요하며, 현재 ECU의 동작 상태를 점검할 수 있는 기능이 요구된다. 이러한 기능을 수행하기 위하여 ECU에 서비스 루틴을 추가하고, 진단 프로그램을 개발하여 성능을 평가하였다.

• Journal of the Korea Society for Simulation
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• v.30 no.1
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• pp.127-137
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• 2021

The method of utilizing the strain and vibration values of rails is primarily used to diagnose the condition of wheels and railroad facilities. The dynamic load is measured under the assumption that the strain of the rail and the load of the railroad vehicle are proportional. Wheel condition is measured under the assumption that the magnitude of the defect and the magnitude of the rail vibration are proportional. However, environmental factors affecting the strain and vibration of the rail such as vehicle speed, wheel load, climate, and track conditions are not reflected, many errors occur depending on the measurement conditions. In this study, the effect of track distortion, which is a major indicator of the track condition among the environmental factors that affect the strain and vibration of the rail, on the strain and vibration of the rail, was examined through dynamic simulation. As a measure to reduce the measurement deviation, the effect of securing additional measurement points was analyzed.

• Annual Conference of KIPS
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• 2011.11a
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• pp.453-454
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• 2011

에너지 플랜트, 화학공장 등 대형 기계설비의 복잡도 증가는 그에 따른 위험요소 복잡도를 증가시키고 있다. 대형 기계설비에 대한 위험 관리와 시스템 신뢰도 향상을 위해서는 위험도 기반의 안전 설계/해석, 상태 감시 및 진단을 이용한 설비 모니터링, 인적 신뢰도 향상이 요구되며 설계 단계에서부터 운영 및 유지보수 단계까지의 시스템 생애주기 전반에 걸친 지속적인 활동을 필요로 한다. 인적 신뢰도를 향상시키기 위해서는 설비 운영자가 응급 또는 이상상황에 대처할 수 있는 능력 즉, 안전사고 대응 능력 확보가 필수적이다. 대상 설비에 대한 실제 훈련은 시간, 비용, 훈련자의 안전 확보가 어렵고 반복적인 훈련 및 평가가 어렵기 때문에 가상 환경을 이용한 안전대응 훈련 플랫폼을 설계하였다. 훈련자에게 몰입형 가상 환경을 제공하기 위하여 가상현실 및 안전사고 시뮬레이션 기술을 이용하였으며 이를 통하여 훈련자의 안전을 유지하면서 안전대응 능력을 향상시킬 수 있다.