• 한국산학기술학회논문지
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• 제16권5호
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• pp.3000-3005
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• 2015

신뢰할 수 있는 공정 감시와 진단은 생산 공정의 안전과 최종제품의 품질을 보장이라는 관점에서 중요하다. 공정진단의 목적은 특정한 공정 이상의 원인을 밝혀내는 것이다. 본 연구에서는 분류기법에 기반한 공정진단 체계를 제시한다. 여기서는 공정데이터를 비선형 데이터 표현기법을 통해 변환함으로써 데이터의 크기를 줄이며 효율적인 데이터 표현이 가능하다. 추가적인 단계로서 공정 데이터의 전처리 과정을 통해 진단에 무관한 공정 패턴을 제거하고 진단 성능을 높이고자 한다. 진단 성능을 평가하기 위해 회분식 공정에 대한 사례연구를 수행한 결과 기존 선형 진단 방법론 및 전처리 과정이 없는 방법론에 비해 향상된 진단 결과를 얻을 수 있었다.

• 산업공학
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• 제25권2호
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• pp.170-177
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• 2012

Process control is essential to operate the semiconductor process efficiently. This paper consider fault classification of semiconductor based cyclic signal for process control. In general, process signal usually take the different pattern depending on some different cause of fault. If faults can be classified by cause of faults, it could improve the process control through a definite and rapid diagnosis. One of the most important thing is a finding definite diagnosis in fault classification, even-though it is classified several times. This paper proposes the method that one-class classifier classify fault causes as each classes. Hotelling T2 chart, kNNDD(k-Nearest Neighbor Data Description), Distance based Novelty Detection are used to perform the one-class classifier. PCA(Principal Component Analysis) is also used to reduce the data dimension because the length of process signal is too long generally. In experiment, it generates the data based real signal patterns from semiconductor process. The objective of this experiment is to compare between the proposed method and SVM(Support Vector Machine). Most of the experiments' results show that proposed method using Distance based Novelty Detection has a good performance in classification and diagnosis problems.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권4호
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• pp.279-284
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• 2007

Nowadays, due to development of automatic control devices and various sensors, one operator can freely handle several remote plants and processes. Automatic diagnosis and warning systems have been adopted in various fields, in order to prepare an operator's absence for patrolling plants. In this paper, a Bayesian networks based on-line diagnosis system is proposed for a wastewater treatment process. Especially, the suggested system is included learning structure, which can continuosly update conditional probabilities in the networks. To evaluate performance of proposed model, we made a lab-scale five-stage step-feed enhanced biological phosphorous removal process plant and applied on-line diagnosis system to this plant in the summer.

• 유공압시스템학회논문집
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• 제6권2호
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• pp.22-30
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• 2009

An in-process method of diagnosing the spool wear of hydraulic servovalves was explored. The diagnostic method discussed in this paper is for force-control hydraulic servo systems. The key principle used is that pressure sensitivity of a servovalve drops as the valve spool wears out so that it is possible to determine the spool condition by monitoring pressure sensitivity. A diagnostic algorithm was developed and evaluated through numerical simulation and experiments. Two major steps of diagnosis are the evaluation of null bias of the servovalve and the approximation of pressure sensitivity, both of which could be successfully done during normal operation of a servo system. The difference between a new servovalve and a worn valve could be clearly detected in-process, and the diagnostic test was found to be repeatable.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1994년도 추계학술대회 논문집
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• pp.670-675
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• 1994

Yhis paper describes a fault diagnosis simulation of the Real-Time Multiple Fault Dignosis System (RTMFDS) for forcasting faults in a system and deciding current machine state from signal information. Comparing with other diagnosis system for single fault,the system developed deals with multiple fault diagnosis,comprising two main parts. One is a remotesignal generating and transimission terminal and the other is a host system for fault diagnosis. Signal generator generate the random fault signal and the image information, and send this information to host. Host consists of various modules and agents such as Signal Processing Module(SPM) for sinal preprocessing, Performence Monotoring Module(PMM) for subsystem performance monitoring, Trigger Module(TM) for multi-triggering subsystem fault diagnosis, Subsystem Fault Diagnosis Agent(SFDA) for receiving trigger signal, formulating subsystem fault D\ulcornerB and initiating diagnosis, Fault Diagnosis Module(FDM) for simulating component fault with Hierarchical Artificial Neural Network (HANN), numerical models and Hofield network,Result Agent(RA) for receiving simulation result and sending to Treatment solver and Graphic Agent(GA). Each agent represents a separate process in UNIX operating system, information exchange and cooperation between agents was doen by IPC(Inter Process Communication : message queue, semaphore, signal, pipe). Numerical models are used to deseribe structure, function and behavior of total system, subsystems and their components. Hierarchical data structure for diagnosing the fault system is implemented by HANN. Signal generation and transmittion was performed on PC. As a host, SUN workstation with X-Windows(Motif)is used for graphic representation.

• 임상간호연구
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• 제14권3호
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• pp.141-151
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• 2008

Purpose: This study aimed at applying a standardized nursing process to adult surgery patients of post anesthetic care unit, and examining the validity of linkages in the measuring index of nursing outcome by which nursing outcome was applied. Method: The subjects were 184 surgery adult patients admitted at the post anesthetic care unit of Y university hospital. This study was used the measured tool developed by Choi et al.(2004) and by Lee (2004) who had already verified a validity based on Johnson and Bulechek's study(2001). Results: The nursing diagnosis of an acute pain, an urinary retention, a nausea, a decreased cardiac output, an ineffective airway clearance and an ineffective airway clearance were used in taking care for patients. The related factors according to the main nursing diagnosis were as the following: an injurious physical factor in an acute pain, reflex are inhibition in an urinary retention, post surgical anesthesia in a nausea, stroke volume change in a decreased cardiac output, secretory stasis in an ineffective airway clearance, pain in an ineffective breathing pattern. Conclusion: The study results could be facilitated in nursing process application for nurses at post anesthetic care unit. Also this study would provide basic data to develop a computerized program for the improvement of nursing process application.

• 지능정보연구
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• 제4권1호
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• pp.115-131
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• 1998

Customer service process is one of the most important processes in today's competitive business environment. Among the various activities of customer service process, equipment malfunction diagnosis activity should be performed fast and accurately. When a customer calls the service center and reports the observed symptoms, he/she describes them in layman's terms. Therefore, the customer-reported symptoms have not been considered helpful information for service representatives. However, in order to perform diagnosis activity fast and accurately, we need to make use of the customer-reported symptoms actively. In this research, we developed three systems called R-EMD (Rule-based Equipment Malfunction Diagnostic system), C-EMD (Case-based Equipment Malfunction Diagnostic system) and R&C-EMD (Rule & Case-based Equipment Malfunction Diagnostic system), each of which diagnoses equipment malfunctions using the customer-reported symptoms. R&C-EMD is a hybrid system that utilizes both rule-based and case-based technologies. The diagnosis rules used in R&C-EMD and R-EMD were not acquired from service manuals or interviews with service representatives. Rater, we extracted them directly from the past diagnosis cases based on symptoms' frequencies. By this way, we were able to overcome the knowledge acquisition bottleneck. Using the real 100 malfunction diagnosis cases, we evaluated the performances of R&C-EMC, R-EMD and C-EMD in terms of speed and accuracy. In diagnosis time, R&C-EMD took longer than R-EMD and shorter than C-EMD. However, R&C-EMC was the best in accuracy.

• 대한안전경영과학회지
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• 제12권2호
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• pp.65-73
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• 2010

In an automated industry PLC plays a central role to control the manufacturing system. Therefore, fault free operation of PLC controlled manufacturing system is essential in order to maximize a firm's productivity. On the contrary, distributed nature of manufacturing system and growing complexity of the PLC programs presented a challenging task of designing a rapid fault finding system for an uninterrupted process operation. Hence, designing an intelligent monitoring, and diagnosis system is needed for smooth functioning of the operation process. In this paper, we propose a method to continuously acquire a stream of PLC signal data from the normal operational PLC-based manufacturing system and to generate diagnosis model from the observed PLC signal data. Consequently, the generated diagnosis model is used for distinguish the possible abnormalities of manufacturing system. To verify the proposed method, we provided a suitable case study of an assembly line.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.233-238
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• 1993

In this paper, two diagnosis algorithms using the simple fuzzy, cognitive map (FCM) that is an useful qualitative model are proposed. The first basic algorithm is considered as a simple transition of Shiozaki's signed directed graph approach to FCM framework. And the second one is an extended version of the basic algorithm. In the extension, three important concepts, modified temporal associative memory (TAM) recall, temporal pattern matching algorithm and hierarchical decomposition are adopted. As the resultant diagnosis scheme takes short computation time, it can be used for on-line fault diagnosis of large scale and complex processes that conventional diagnosis methods cannot be applied. The diagnosis system can be trained by the basic algorithm and generates FCM model for every experienced process fault. In on-line application, the self-generated fault model FCM generates predicted pattern sequences, which are compared with observed pattern sequences to declare the origin of fault. In practical case, observed pattern sequences depend on transport time. So if predicted pattern sequences are different from observed ones, the time weighted FCM with transport delay can be used to generate predicted ones. The fault diagnosis procedure can be completed during the actual propagation since pattern sequences of tvo different faults do not coincide in general.

• 대한한의학원전학회지
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• 제23권1호
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• pp.317-319
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• 2010

The collateral diagnosis method is unique in Traditional Chinese Medicine diagnosis methods which has important clinical value. In my article, the contents of the "Hwangjenaegyeong(黃帝內經)" related to this method is discussed. According to the site of inspection in the diagnosis process, there are five types as following: inspection of the face and surface, the thenar, the orifices, abdominal collaterals and finally the index finger of children. This method can inspire clinical practitioners.