• Journal of Information Processing Systems
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• 제18권6호
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• pp.755-762
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• 2022

The existing electrical engineering design method and the dynamic objective function in the application process of automatic control system fail to meet the unbounded condition, which affects the control tracking accuracy. In order to improve the tracking control accuracy, this paper studies the electrical engineering design method based on neural network and the application of automatic control system. This paper analyzes the structure and working mechanism of electrical engineering automation control system by an automation control model with main control objectives. Following the analysis, an optimal solution of controllability design and fault-tolerant control is figured out. The automatic control power coefficient is distributed based on an ideal control effect of system. According to the distribution results, an automatic control algorithm is based on neural network for accurate control. The experimental results show that the electrical automation control method based on neural network can significantly reduce the control following error to 3.62%, improve the accuracy of the electrical automation tracking control, thus meeting the actual production needs of electrical engineering automation control system.

• 대한임베디드공학회논문지
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• 제19권1호
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• pp.57-64
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• 2024

In the industrial field, robots are used to increase productivity by replacing labors with dangerous, difficult, and hard tasks. However, failures of individual industrial robots in the entire production process may cause product defects or malfunctions, and may cause dangerous disasters in the case of manufacturing parts used in automobiles and aircrafts. Although requirements for early diagnosis of industrial robot failures are steadily increasing, there are many limitations in early detection. This paper introduces methods for diagnosing robot failures using sound-based data and deep learning. This paper also analyzes, compares, and evaluates the performance of failure diagnosis using various deep learning technologies. Furthermore, in order to improve the performance of the fault diagnosis system using deep learning technology, we propose a method to increase the accuracy of fault diagnosis based on an inference window. When adopting the inference window of deep learning, the accuracy of the failure diagnosis was increased up to 94%.

• 대한산업공학회지
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• 제36권3호
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• pp.154-163
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• 2010

Process monitoring of output variables affecting final performance have been mainly executed in semiconductor manufacturing process. However, even earlier detection of causes of output variation cannot completely prevent yield loss because a number of wafers after detecting them must be re-processed or cast away. Semiconductor manufacturers have put more attention toward monitoring process inputs to prevent yield loss by early detecting change-point of the process. In the paper, we propose the method to efficiently monitor functional input variables in multi-phase semiconductor manufacturing process. Measured input variables in the multi-phase process tend to be of functional structured form. After data pre-processing for these functional input data, change-point analysis is practiced to the pre-processed data set. If process variation occurs, key variables affecting process variation are selected using contribution plot for monitoring efficiency. To evaluate the propriety of proposed monitoring method, we used real data set in semiconductor manufacturing process. The experiment shows that the proposed method has better performance than previous output monitoring method in terms of fault detection and process monitoring.

• Industrial Engineering and Management Systems
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• 제12권2호
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• pp.112-117
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• 2013

The $\overline{x}$ control chart for the process mean and either the R or s control chart for the process dispersion have been used together to monitor the manufacturing processes. However, it has been pointed out that this procedure is flawed by a fault that makes it difficult to capture the behavior of process condition visually by considering the relationship between the shift in the process mean and the change in the process dispersion because the respective characteristics are monitored by an individual control chart in parallel. Then, the ($\overline{x}$, s) control chart has been proposed to enable the process managers to monitor the changes in the process mean, process dispersion, or both. On the one hand, identifying which process parameters are responsible for out-of-control condition of process is one of the important issues in the process management. It is especially important in the ($\overline{x}$, s) control chart where some parameters are monitored at a single plane. The previous literature has proposed the multiple decision method based on the statistical hypothesis tests to identify the parameters responsible for out-of-control condition. In this paper, we propose how to identify parameters responsible for out-of-control condition using the information criterion. Then, the effectiveness of proposed method is shown through some numerical experiments.

• 반도체디스플레이기술학회지
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• 제18권1호
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• pp.5-9
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• 2019

Optical emission spectroscopy is used to identify chemical species and monitor the changes of process results during the plasma process. However, plasma process monitoring or fault detection by using emission signal variation monitoring is vulnerable to background signal fluctuations. IR heaters are used in semiconductor manufacturing chambers where high temperature uniformity and fast response are required. During the process, the IR lamp output fluctuates to maintain a stable process temperature. This IR signal fluctuation reacts as a background signal fluctuation to the spectrometer. In this research, we evaluate the effect of infrared background signal fluctuation on plasma process monitoring and improve the plasma process monitoring accuracy by using simple infrared background signal subtraction method. The effect of infrared background signal fluctuation on plasma process monitoring was evaluated on $SiO_2$ PECVD process. Comparing the $SiO_2$ film thickness and the measured emission line intensity from the by-product molecules, the effect of infrared background signal on plasma process monitoring and the necessity of background signal subtraction method were confirmed.

• 한국광물학회지
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• 제25권1호
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• pp.23-36
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• 2012

경주시 양북면 단층대의 각력암에서 로몬타이트와 아듈라리아가 산출되는데, 이들의 형태적 특징은 단층활동과 관련된 열수와의 반응으로 형성되었음을 지시한다. 로몬타이트의 산출은 충분한 물이 존재하는 각력대에 알칼리원소가 풍부하게 공급되었음을 의미한다. 로몬타이트는 신장된 주상형의 결정이 특징적인데 길이와 폭의 비는 대부분 5~10 : 1 범위이다. 자형의 로몬타이트, 아듈라리아는 Ca-사장석을 교대하거나, 유체로부터 침전되어 이차적으로 형성되었는데, 최후기 단계에서 비교적 빠르게 형성되었다. 단층파쇄작용으로 인해 투수성이 높아진 화강암질 모암은 열수와 반응하여 Ca, K와 같은 알칼리원소를 용탈시켰으며, 중성-약알칼리성의 열수용액으로부터 각력대에 로몬타이트, 아듈라리아가 침전되었다. 단층파쇄대에서 흔히 발달하는 로몬타이트와 아듈라리아와 같은 저온성 변질광물의 형성과정과 성인은 천열수광상에서 흔히 일어나는 모암변질, 열수반응과 유사하다. 단층대에서 생성되는 저온성, 이차적 아듈라리아의 형태적 특징과 화학조성은 조암광물의 K-장석류와 구분되므로 국내의 단층대에서 흔히 산출하는 K-장석은 아듈라리아일 가능성이 높다.

• Transactions on Electrical and Electronic Materials
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• 제14권2호
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• pp.71-77
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• 2013

Advanced semiconductor manufacturing technology requires methods to maximize tool efficiency and improve product quality by reducing process variability. Real-time plasma process monitoring and diagnosis have become crucial for fault detection and classification (FDC) and advanced process control (APC). Additional sensors may increase the accuracy of detection of process anomalies, and optical monitoring methods are non-invasive. In this paper, we propose the use of a chromatic data acquisition system for real-time in-situ plasma process monitoring called the Plasma Eyes Chromatic System (PECS). The proposed system was initially tested in a six-inch research tool, and it was then further evaluated for its potential to detect process anomalies in an eight-inch production tool for etching blanket oxide films. Chromatic representation of the PECS output shows a clear correlation with small changes in process parameters, such as RF power, pressure, and gas flow. We also present how the PECS may be adapted as an in-situ plasma arc detector. The proposed system can provide useful indications of a faulty process in a timely and non-invasive manner for successful run-to-run (R2R) control and FDC.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 C
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• pp.1772-1774
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• 2004

Because the physics occurring during an interruption process is not well known, it is not easy to analyze the characteristics of a self-blast circuit breaker neither theoretically nor experimentally. Fortunately the available computational power and the numerical method improved recently make it possible to predict an interruption process as precisely and fast as possible. Therefore many researches using computational methods have been done for the interruption process of interrupters and applied to extend the information such as thermal and dielectric reignition. In this paper, we have simulated the interruption process of SF6 self-blast circuit breakers with the arc plasma during the fault interruption of a 10 kA current. The CFD program used here is coupled with the electromagnetic field analysis, the radiation model and the effects of turbulence. Through this work, we have get further information about the thermal performance as well as the behavior of the arc. The results have been compared with the measured arc voltage.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.250-255
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• 1993

In this paper, for detection and isolation of instrument and process faults related with steam generator(S/G) in nuclear power plant, two types of observers are designed based on the linearized dynamic model of S/G : a bank of Dedicated Observers (DOS) for instrument faults detection and a bank of Unknown Input Observers(UIO) for process faults detection. And then, they are combined to decide which one between the above two faults occurs. In principle, the failure in ith instrument(process) can be isolated by monitoring the error between the ith output and its estimation obtained from the ith DOS(UIO). It is shown via computer simulations that the present scheme is feasible in finding out the source of a fault.

• 전자공학회논문지B
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• 제32B권12호
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• pp.1545-1551
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• 1995

As the modern industrial processes become more complex, it is getting more difficult to model and control the processes. Naturally, an advanced type of DCS(Distributed Control System) with higher level functions is being sought. Advanced DCS is a DCS with advanced functions such as fault diagnosis, GPC(Generalized Predictive Control), NN(Neural Network), and Fuzzy Control. In this thesis, we have studied a fuzzy control algorithm for realizing an advanced DCS. Its algorithm is implemented in a form of function code which is a process control language, being used by the industrial engineers. To verify the realized function code of the fuzzy control, the function code is applied to a continuous casting process of the Pohang Iron & Steel Works in Kwangyang. The rules of the fuzzy control were collected via interviews of the field operators and their operation documents. Finally under a real-time operating system environment, usability of the function code of the fuzzy control is shown via simulation for the continuous casting process.