• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2612-2616
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• 2003

Method for performance monitoring and diagnosis of a MIMO control system has been studied aiming at application to model predictive control (MPC) for industrial processes. The performance monitoring part is designed on the basis of the traditional SPC/SQC method. To meet the underlying premise of Schwart chart observation that the observed variable should be univariate and independent, the process variables are decorrelated temporally as well as spatially before monitoring. The diagnosis part was designed to identify the root of performance degradation among the controller, process, and disturbance. For this, a method to estimate the model-error and disturbance signal has been devised. The proposed methods were evaluated through numerical examples.

• 한국압력기기공학회 논문집
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• 제19권1호
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• pp.6-10
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• 2023

Only about 10% of selected equipment in nuclear power plants are monitored by wiring to address failures or problems caused by vibration. The purpose is primarily for preventive maintenance, not for predictive maintenance. This paper shows that vibration monitoring and diagnosis using Industrial 4.0 enables the complete predictive maintenance for all vibrating equipments in nuclear power plants with the convergence of internet of things; wireless technology, big data through periodic collection and artificial intelligence. Predictive maintenance using wireless technology is possible in all areas of nuclear power plants and in all systems, but it should satisfy regulatory guides on electromagnetic interference and cyber security.

• 한국가스학회지
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• 제26권4호
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• pp.41-57
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• 2022

기후변화 대응에 따른 전세계적인 탄소중립 이행에 대한 요구는 수출주도형 경제구조와 온실가스 수출국가로 분류되어 있는 우리나라를 비롯한 일부 국가들에게 탄소 무역장벽 대응방안을 마련해야 하는 상황에 놓여있다. 따라서, 탄소중립 이행 모델의 적용을 위해 예측 가능한 방법 중에 하나인 디지털 전환을 앞당겨 도입해야 한다. 주요산업 중 하나인, 첨단제조산업에서 쓰이는 산업용 가스 제조시설과 친환경 에너지로 부각되고 있는 수소 가스시설에 디지털 기술을 적용하여, 이상감지 및 진단 서비스를 클라우드 기반의 조업지식이 포함된 예측진단 모니터링 기술 동향을 소개한다. 단순히 실시간 설비 상태를 모니터링하는 것이 아닌, 최적화와 증강현실 기술, 그리고 IoT 와 AI 지식 추론 등을 통해 이상진단 예측 모니터링의 구축 방향을 확인하고, 탄소중립 이행의 사각지대에 놓여 있는, 중소·중견 기업의 경제성과 효율성이 부합되는, 엔지니어링 도메인의 합의된 지식과 예측진단 모니터링 등의 기술 보급 가능함을 살펴 볼 수 있다. 최고 수준의 ICT 기술을 바탕으로 탄소배출 무역장벽에 따른 대응 방안을 모색하는 하나의 방안으로 활용되길 바라며, 해당 기술의 도입을 통해, 탄소중립 이행에 따른 중소·중견기업의 마중물이 될 것이다.

• 융합신호처리학회논문지
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• 제6권3호
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• pp.143-147
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• 2005

운전 중인 기계들의 안전 운전과 예지 보전을 위한 설비의 고장 감지 및 진단과 상태감시는 산업 현장에서 중요한 역할을 담당하고 있다. 이러한 설비의 많은 기기들은 회전기기로 이루어져 있으며 회전기기의 고장진단은 오랜 기간 많은 분야에서 연구되고 있다. 본 연구에서는 회전기기의 고장신호는 주파수 영역의 신호의 변화로 나타난다는 특징을 이용하여 보다 효율적인 주파수 영역에서의 신호 해석을 위하여 Linear Predictive Coding(LPC) 계수를 이용하였다. 사용된 데이터는 회전기기의 고장 신호의 습득을 용이하게 하기 위하여 유도전동기에 인위적인 고장재현을 통하여 습득된 진동 신호를 사용하였다. 제안된 시스템은 LPC 분석을 사용하여 일반적으로 사용되는 주파수 영역 상에서의 다른 해석 방법들보다 빠른 시간에 연산 결과를 도출할 수 있는 장점을 가질 수 있었으며, 성공적인 실험 결과를 얻을 수 있었다.

• 한국압력기기공학회 논문집
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• 제19권1호
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• pp.44-51
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• 2023

The RCP is one of the main components in nuclear power plants and plays an important role in circulating coolant to the RCS system. Currently, nuclear plants are monitored using various monitoring systems. However, since they operate independently according to their functional purpose, it is not able to analyze vibration and operation/performance information comprehensively, and thus failure diagnosis accuracy is limited. In addition, these systems do not provide some important information (such as fault type, parts and cause) necessary for emergency actions, but provide only alarm information. To improve these technical problems, this study proposes a diagnosis technique (M/L, Rule-based model, Data-driven model, Narrow band model) and methodology for comprehensive analysis.

• 한국산업안전학회:학술대회논문집
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• 한국안전학회 2001년도 공동학술대회
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• pp.31-35
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• 2001

Recent trends require that access to the device/equipment information be provided from several locations or anywhere in the enterprise. One example is virtual machine/manufacturing system (VMS) where predictive maintenance is performed both on factory floor and in remote site through internet ［1］. Internet access is increasingly available and affordable, and along with the "internet" is the backbone of modern enterprise data networks. Typical functions of such a system includes monitoring and control for diagnosis and remedy action in realizing preventive maintenance.(omitted)

• 한국안전학회지
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• 제34권4호
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• pp.59-67
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• 2019

Since it is really hard to construct process models based on prior process knowledges, various statistical approaches have been employed to build fault diagnosis models. However, the crucial drawback of these approaches is that the solutions may vary according to the fault magnitude, even if the same fault occurs. In this study, the parameter monitoring approach is suggested. When a fault occurs in a chemical process, this leads to trigger the change of a process model and the monitoring parameters of process models is able to provide the efficient fault diagnosis model. A few important variables are selected and their predictive models are constructed by partial least square (PLS) method. The Euclidean norms of parameters of PLS models are estimated and a fault diagnosis can be performed as comparing with parameters of PLS models based on normal operational conditions. To improve the monitoring performance, cumulative summation (CUSUM) control chart is employed and the changes of model parameters are recorded to identify the type of an unknown fault. To verify the efficacy of the proposed model, Tennessee Eastman (TE) process is tested and this model can be easily applied to other complex processes.

• 유공압시스템학회논문집
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• 제1권2호
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• pp.1-7
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• 2004

In-process diagnosis is essential to achieve predictive maintenance in industrial plants. An in- process diagnosis method was proposed for hydraulic servo systems, which was based upon leakage flow measurement. Leakage due to servovalve wear was analysed and modeled mathematically far computer simulation work. The key idea of diagnosis algorithm is that when monitoring signals, such as servovalve input and load displacement are in steady states, the return-line flow of hydraulic servo systems can be regarded as null-leakage of servovalve. Virtual experiments were performed to ensure effectiveness of the proposed diagnosis method.

• 한국윤활학회:학술대회논문집
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• 한국윤활학회 1998년도 제27회 춘계학술대회
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• pp.321-327
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• 1998

This paper presented a case study on the application of on-line wear monitoring technique to a high duty air-turbo-compressor system. Main objects monitored were a gear unit and metal bearings, both shown frequent troubles due to the severe operation conditions at heavy dynamic load. The air-turbo-compressor system needs secure condition monitoring because it is one of the main utilities in steel making industry. Temperature and vibration characteristics have been mainly on-line monitored in this system for a predictive maintenance; however, it has been shown that they are not fairly good enough to give an early warning prior to the machine failure. In this work, an on-line Opto Magnetic Detector(OMD) was implemented for an on-line wear monitoring, which quantitatively measured the contamination level of both ferrous and non-ferrous wear particles by detecting the change in optical density of used oil. Results showed that the application of on-line OMD system was satisfactory in diagnosis of the machine system.

• International Journal of Aeronautical and Space Sciences
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• 제15권2호
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• pp.123-137
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• 2014

The aviation gas turbine is composed of many expensive and highly precise parts and operated in high pressure and temperature gas. When breakdown or performance deterioration occurs due to the hostile environment and component degradation, it severely influences the aircraft operation. Recently to minimize this problem the third generation of predictive maintenance known as condition based maintenance has been developed. This method not only monitors the engine condition and diagnoses the engine faults but also gives proper maintenance advice. Therefore it can maximize the availability and minimize the maintenance cost. The advanced gas turbine health monitoring method is classified into model based diagnosis (such as observers, parity equations, parameter estimation and Gas Path Analysis (GPA)) and soft computing diagnosis (such as expert system, fuzzy logic, Neural Networks (NNs) and Genetic Algorithms (GA)). The overview shows an introduction, advantages, and disadvantages of each advanced engine health monitoring method. In addition, some practical gas turbine health monitoring application examples using the GPA methods and the artificial intelligent methods including fuzzy logic, NNs and GA developed by the author are presented.