• 산업공학
• /
• 제22권2호
• /
• pp.126-134
• /
• 2009

As product quality and yield are essential factors in semiconductor manufacturing, monitoring the main manufacturing steps is a critical task. For the purpose, FDC(Fault detection and classification) is used for diagnosing fault states in the processes by monitoring data stream collected by equipment sensors. This paper proposes an FDC model based on decision tree which provides if-then classification rules for causal analysis of the processing results. Unlike previous decision tree approaches, we reflect the structural aspect of the data stream to FDC. For this, we segment the data stream into multiple subregions, define structural features for each subregion, and select the features which have high relevance to results of the process and low redundancy to other features. As the result, we can construct simple, but highly accurate FDC model. Experiments using the data stream collected from etching process show that the proposed method is able to classify normal/abnormal states with high accuracy.

• 한국정보통신학회논문지
• /
• 제10권5호
• /
• pp.962-969
• /
• 2006

현재 고밀도 반도체제작 환경에서는 반작용적인 이온 식각 과정(reactive ion etching)에서의 생산성을 극대화하기 위해서 비정상적인 공정장비를 발견하는 것이 매우 중요하다. 생산과정에서 오류발견의 중요성을 설명하기 위해 Support Vector Machine (SVM)은 실시간으로 공정오류에 대한 판단을 위해 사용되었다. 반작용적인 이온 식각도구 데이터는 59개 변수들로 구성된 반도체 공정장비로부터 얻는다. 각각의 변수들은 초당 10개의 데이터로 구성되어있다. 식각 런의 11개의 파라미터에 대한 모델을 만들기 위해 baseline런으로부터 얻은 데이터로 SVM모델을 구성하고 정상 런데이터와 비정상 런데이터로 SVM모델을 검증한다. 통계적 공정제어에서 흔히 이용되는 관리한계를 도입하여 정상데이터가 내재하고 있는 램덤변화율이 반영된 SVM 모델 기반의 관리 한계를 수립하고, 그 관리 한계를 바탕으로 오류발견을 실행한다. SVM을 이용함으로써 RIE의 오류발견은 run to run 기반에 정상 런데이터는 0% 오류율이 증명되었다.

• International Journal of Reliability and Applications
• /
• 제17권2호
• /
• pp.149-158
• /
• 2016

We are enjoying a very comfortable life thanks to modern civilization, however, comfort is not guaranteed to us. Development of software system is a difficult and complex process. Therefore, the main focus of software development is on improving the reliability and stability of a software system. We have become aware of the importance of developing software reliability models and have begun to develop software reliability models. NHPP software reliability models have been developed through the fault intensity rate function and the mean value functions within a controlled testing environment to estimate reliability metrics such as the number of residual faults, failure rate, and reliability of the software. In this paper, we present a new NHPP software reliability model with Burr Type III fault detection rate, and present the goodness-of-fit of the fault detection rate software reliability model and other NHPP models based on two datasets of software testing data. The results show that the proposed model fits significantly better than other NHPP software reliability models.

• 한국초전도ㆍ저온공학회논문지
• /
• 제10권1호
• /
• pp.24-27
• /
• 2008

For the development of superconducting fault current limiters(SFCLs) having large current capacity, we fabricated an SFCL element that consists of Bi-2212 superconductor and Cu-Ni alloy tubes. First, Ag was plated on the surface of the Bi-2212 for the enhancement of soldering process. On the Ag-plated Bi-2212 tube, a Cu-Ni alloy tube was soldered using optimized solders and soldering conditions. The BSCCO/Cu-Ni composite was processed mechanically to have a helical shape for the improvement of the SFCL characteristics. The total current path of the SFCL element was 1330 mm long with 12 turns, and had critical current of 340 A at 77 K. Finally, we carried out the fault test using the fabricated SFCL element. It showed successful current limiting performance under the fault condition of 50 $V_{rms}$ and 5.5 kA. From the results, the rated voltage of the SFCL element was decided to be 0.4 V/cm, and the power capacity was 12 kVA at 77 K. The fabrication process of the SFCL and the fault test results will be presented.

• 한국산업융합학회 논문집
• /
• 제7권2호
• /
• pp.199-205
• /
• 2004

This paper describes the fault diagnosis method to order to construct CIM in complex system with hierarchical structure similar to human body structure. Complex system is divided into unit, item and component. For diagnosing this hierarchical complex system, it is necessary to implement a special neural network. Fault diagnosis system can forecast faults in a system and decide from the signal information of current machine state. Comparing with other diagnosis system for a single fault, the developed system deals with multiple fault diagnosis, comprising hierarchical neural network (HNN). HNN consists of four level neural network, i.e. first is fault symptom classification and second fault diagnosis for item, third is symptom classification and forth fault diagnosis for component. UNIX IPC is used for implementing HNN with multitasking and message transfer between processes in SUN workstation with X-Windows (Motif). We tested HNN at four units, seven items per unit, seven components per item in a complex system. Each one neural network represents a separate process in UNIX operating system, information exchanging and cooperating between each neural network was done by message queue.

• 전기학회논문지P
• /
• 제57권2호
• /
• pp.91-95
• /
• 2008

The large AC generator fault may lead to large impacts or perturbations in power system. The generator protection control systems in Korea have been imported and operated through a turn-key from overseas entirely. Therefore a study of the generator protection field has in urgent need for a stable operation of the imported goods. In present, the algorithm using the current ratio differential relaying based DFT for stator winding protection or a fault detection had been applied that of internal fault protection of a generator. the DFT used for the analysis of transient state signal conventionally had defects losing a time information in the course of transforming a target signal to frequency domain. In this paper, the discrete wavelet transform (DWT) was applied a fault detection of the generator being superior to a transient state signal analysis and being easy to real time realization. The fault signals after executing a terminal fault modeling collect using a MATLAB package, and calculate the wavelet coefficients through the process of a muiti-level decomposition (MLD). The proposed algorithm for a fault detection using the Daubechies WT (wavelet transform) was executed with a C language and the commend line function for the real time realization after analyzing MATLAB's graphical interface. The advanced technique had improved faster a speed of fault discrimination than a conventional DFR based on DFT.

• Journal of Electrical Engineering and Technology
• /
• 제12권2호
• /
• pp.886-889
• /
• 2017

An active Fault Tolerant Model Predictive Control (FTMPC) using Fuzzy scheduler is developed. Fault tolerant Control (FTC) system stages are broadly classified into two namely Fault Detection and Isolation (FDI) and fault accommodation. Basically, the faults are identified by means of state estimation techniques. Then using the decision based approach it is isolated. This is usually performed using soft computing techniques. Fuzzy Decision Making (FDM) system classifies the faults. After identification and classification of the faults, the model is selected by using the information obtained from FDI. Then this model is fed into FTC in the form of MPC scheme by Takagi-Sugeno Fuzzy scheduler. The Fault tolerance is performed by switching the appropriate model for each identified faults. Thus by incorporating the fuzzy scheduled based FTC it becomes more efficient. The system will be thereafter able to detect the faults, isolate it and also able to accommodate the faults in the sensors and actuators of the Continuous Stirred Tank Reactor (CSTR) process while the conventional MPC does not have the ability to perform it.

• 대한전기학회논문지:시스템및제어부문D
• /
• 제49권12호
• /
• pp.643-650
• /
• 2000

Using neural network approach, the diagnosis of faults in industrial process that requires observing multiple data simultaneously are studied. Two-stage diagnosis is proposed to analyze system faults. By using neural network, the first stage detects the dynamic trend of each normalized date patterns by comparing a proposed pattern. Instead of using neural network, the difference between stored fault pattern and real time data is used for fault diagnosis in the second stage. This method reduces the amount of calculation and saves storing space. Also, we dealt with unknown faults by normalizing the data and calculating the difference between the value of steady state and the data in case of fault. A model of tank reactor is given to verify that the proposed method is useful and effective to noise.

• 대한기계학회논문집A
• /
• 제21권3호
• /
• pp.470-476
• /
• 1997

The dynamic characteristics of industrial processes frequently cause an abnormal situation which is undesirable in terms of the productivity and the safety of workers. The goal of fault-tolerance is to continue performing certain activities even after the failure of some system cononents. A fault-tolerant intelligent monitoring and control system which is robust under disturbances is proposed in this paper. Specifically, the fault-tolerant monitoring scheme proposed consists of two process models and the inference module to preserve such a robustness. The results of turning experiments demonstrate the effectiveness of the fault-tolerant scheme in the presence of built-up edge.

• 전기학회논문지
• /
• 제59권12호
• /
• pp.2306-2313
• /
• 2010

This paper describes a model based fault detection algorithm for an Advanced ESC System which consists of Hydraulic Control Unit (HCU) with built-in wheel pressure sensors. Advanced ESC System can be used for various value-added functions such as Stop & Go Function and Regenerative Brake Function. Therefore, HCU must have a reliable fault detection. Due to the huge amount of sensor signals, existing specific sensor based fault detection of HCU cannot guarantee the safety of vehicle. However, proposed algorithm dose not require the sensors. When model based fault detection algorithm detects severe failures of the HCU, it warns the driver in advance to prevent accidents due to the failures. For this purpose, a mathematical model is developed and validated in comparison to actual data. Simulation results and data acquired from an actual system are compared with each other to obtain the information needed for the fault detection process.