• 유공압시스템학회논문집
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• 제3권1호
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• pp.7-14
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• 2006

In this paper, an in-process diagnosis method for performance of position control servo system was studied, which was based upon null bias, slew-rate ratio and delay time measurement. Slew-rate ratio and delay time were analyzed by theoretical analysis, computer simulation and experiment. As a result of these analysis, when spool of servovalve was weared, slew-rate ratio was decreased and delay time was increased.

• 반도체디스플레이기술학회지
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• 제9권4호
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• pp.93-97
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• 2010

Fast incipient fault diagnosis is becoming one of the key requirements for economical and optimal process operation management in high-tech industries. Artificial neural networks have been used to detect faults for a number of years and shown to be highly successful in this application area. This paper presents a novel test technique for fault detection and classification for module process of TFT-LCD manufacture using support vector machines (SVMs). In order to evaluate SVMs, this paper examines the performance of the proposed method by comparing it with that of multilayer perception, one of the artificial neural network techniques, based on real benchmarking data.

• 대한치과의사협회지
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• 제54권9호
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• pp.704-711
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• 2016

Radiologic images in dentistry are essential to perform the diagnosis, treatment, and tracking process of prognosis, thus the ability of accurate evaluation in the diagnostic images is requested for dental clinician. Radiologic interpretation means recognition of a normality and an abnormality and to report the possible diagnosis and differential diagnosis list. Therefore, dental clinicians should be familiar with the basic principle of interpretation of intraoral and extraoral radiographic images primarily used in dental clinics. Recently, dental cone beam CT is widely used for diagnositc process, thus understanding the three dimensional images is requested. The objective of this manuscript is to help the dental clinicians to interpret accurately the diagnostic images by introducing the basic principles of the step by step analytic process in the appearance of a lesion.

• Journal of Information Processing Systems
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• 제17권2호
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• pp.242-252
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• 2021

Following the intuition that the local information in time instances is hardly incorporated into the posterior sequence in long short-term memory (LSTM), this paper proposes an attention augmented mechanism for fault diagnosis of the complex chemical process data. Unlike conventional fault diagnosis and classification methods, an attention mechanism layer architecture is introduced to detect and focus on local temporal information. The augmented deep network results preserve each local instance's importance and contribution and allow the interpretable feature representation and classification simultaneously. The comprehensive comparative analyses demonstrate that the developed model has a high-quality fault classification rate of 95.49%, on average. The results are comparable to those obtained using various other techniques for the Tennessee Eastman benchmark process.

• 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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 C
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• pp.1136-1138
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• 1999

This paper describes a new method of the modeling of protection system and fault diagnosis in power systems using Petri nets. The Petri net models of protection system are compose of the operating process of protective devices and the fault diagnosis process. Fault diagnosis model which makes use of the nature of Petri net is developed to overcome the drawbacks of methods that depend on operator knowledge. The proposed method can reduce processing time and increase accuracy when compared with the traditional methods. And also this method covers online processing of real-time data from SCADA.

• Transactions on Control, Automation and Systems Engineering
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• 제1권1호
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• pp.54-61
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• 1999

FCM(Fuzzy Cognitive Map) is proposed for representing causal reasoning. Its structure allows systematic causal reasoning through a forward inference. By using the FCM, authors have proposed FCM-based fault diagnostic algorithm. However, it can offer multiple interpretations for a single fault. In process engineering, as experience accumulated, some form of quantitative process knowledge is available. If this information can be integrated into the FCM-based fault diagnosis, the diagnostic resolution can be further improved. The purpose of this paper is to propose an enhanced FCM-based fault diagnostic scheme. Firstly, the membership function of fuzzy set theory is used to integrate quantitative knowledge into the FCM-based diagnostic scheme. Secondly, modified TAM recall procedure is proposed. Considering that the integration of quantitative knowledge into FCM-based diagnosis requires a great deal of engineering efforts, thirdly, an automated procedure for fusing the quantitative knowledge into FCM-based diagnosis is proposed by utilizing self-learning feature of neural network. Finally, the proposed diagnostic scheme has been tested by simulation on the two-tank system.

• 한국생산제조학회지
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• 제22권2호
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• pp.287-291
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• 2013

Fault Diagnosis is a process of detecting and isolating faults in a system. On demanding for safety and high reliability systems make it important for some reasons such as economical and environmental incentives. Especially embedded technology and IT technology combined with precise sensing techniques has been doing well developed and applied to fault diagnosis and prognosis in industrial systems like as automotive, ship, heavy industry and aerospace as well. This paper, as an empirical application of diesel engine, presents a method how to get raw data from physical systems, what to consider for successful implementation and which theoretic mathematical models should be applied. In a sense of system level Adaptive Filtering (we call Modified Kalman Filter) and a unit of part level Hidden Markov Process was developed and applied.

• 한국기계가공학회지
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• 제4권3호
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• pp.57-62
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• 2005

Neural networks, which have learning and self-organizing abilities, can be advantageously used in the pattern recognition. Neural network techniques have been widely used in monitoring and diagnosis, and compare favourable with traditional statistical pattern recognition algorithms, heuristic rule-based approaches, and fuzzy logic approaches. In this study the fault diagnosis of the face-milling using the artificial neural network was investigated. After training, the sample which measure load current was monitored by constant output results.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 A
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• pp.88-90
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• 1998

The conceptual design of turbine-driven air-cooled 100 MW generator is presented. The generators rating is 100 MW 3-phase 2pole 13.8 kV 0.85 pf 60 Hz. The conceptual design is described by the process of designing the stator, rotor, and obtaining some of equivalent circuit parameters. The design process starts from the output coefficient G, and utilizes the classical design equations with parameters used in the modern designs. The slot dimensions of the stator and rotor are obtained with their respective winding dimensions.