• Journal of Mechanical Science and Technology
• /
• v.15 no.11
• /
• pp.1482-1489
• /
• 2001

Thermally-induced errors originating from machine tool errors have received significant attention recently because high speed and precise machining is now the principal trend in manufacturing proce sses using CNC machine tools. Since the thermal error model is generally a function of temperature, the thermal error compensation system contains temperature sensors with the same number of temperature variables. The minimization of the number of variables in the thermal error model can affect the economical efficiency and the possibility of unexpected sensor fault in a error compensation system. This paper presents a thermal error model with minimum number of variables using a fuzzy logic strategy. The proposed method using a fuzzy logic strategy does not require any information about the characteristics of the plant contrary to numerical analysis techniques, but the developed thermal error model guarantees good prediction performance. The proposed modeling method can also be applied to any type of CNC machine tool if a combination of the possible input variables is determined because the error model parameters are only calculated mathematically-based on the number of temperature variables.

• Proceedings of the Safety Management and Science Conference
• /
• 1999.11a
• /
• pp.109-121
• /
• 1999

플랜트 및 설비가 대규모, 정교화, 복잡화 될수록 이로 인한 고장 및 오류에 의한 피해가 막대하기 때문에, 시스템의 신뢰성, 보전성 및 안전성 향상과 제품 품질 향상을 추구 및 안전성 유지에 대한 관심이 고조되고 있다. 고장진단은 잠재적으로 노이즈를 가지고 있다고 생각되는 데이터의 해석에 근거하여 시스템의 고장을 찾는 일련의 체계적이고 통합된 방법이다. 그러나 대부분의 방법들이 이진 논리에 기초를 둔 추론으로 불확실성을 제대로 결과에 반영하지 못하고 있다. 본 논문에서는 예방정비의 관점에서 시스템에 내재된 다양한 불확실성을 효율적으로 처리하기 위해 전문가의 직관과 경험등을 기초로 하여 언어학적 변량을 취하고, 이를 퍼지 기법을 이용하여 정량화 함으로써 불확실성을 고려한 판단이 가능하게 하는 퍼지 전문가 시스템을 제안한다.

• Journal of Aerospace System Engineering
• /
• v.8 no.4
• /
• pp.7-17
• /
• 2014

Objective of this paper is to introduce a new technology known as prognostics and health management (PHM) which enables a real-time life prediction for safety critical systems under extreme loading conditions. In the PHM, Bayesian framework is employed to account for uncertainties and probabilities arising in the overall process including condition monitoring, fault severity estimation and failure predictions. Three applications - aircraft fuselage crack, gearbox spall and battery capacity degradation are taken to illustrate the approach, in which the life is predicted and validated by end-of-life results. The PHM technology may allow new maintenance strategy that achieves higher degree of safety while reducing the cost in effective manner.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.15 no.1
• /
• pp.93-101
• /
• 2002

In this study, the stator form-winding sample coils based on silicone resin and polyimide were made for fault prediction and reliability estimation on the C-Class(200$\^{C}$ ) insulation system of traction motors. The complex accelerative degradation was periodically performed during 10 cycles, which was composed of thermal stress, fast rising surge voltage, vibration, water immersion and overvoltage applying. After aging of 10 cycles, the condition diagnosis test such as insulation resistance '||'&'||' polarization index, capacitance '||'&'||' dielectric loss and partial discharge properties were investigated in the temperature range of 20 ∼ 160$\^{C}$. Relationship among condition diagnosis tests was analyzed to find a dominative degradation factor and an insulation state at end-life point.

• Proceedings of the KIEE Conference
• /
• 2003.11a
• /
• pp.327-330
• /
• 2003

This paper proposes an estimation algorithm for the generating power of distributed generations(DG) interconnected with distribution networks. These days, DG are rapidly increasing and most of them are interconnected with distribution networks. The DG can supply power into the distribution network, which may make significant impact on fault current and the protection scheme of the interconnected distribution networks. Generally these influences of DG is proportioned as the distributed generator's power. Therefore, it is important to forecast the output power of distributed generator in PCC(point of common coupling). This paper presents the prediction method of DG's power by monitoring the current and phase difference.

• Proceedings of the KIPE Conference
• /
• 2015.07a
• /
• pp.15-16
• /
• 2015

According to 2014 fire statistical yearbook in the National Fire Data System, a main cause of fire is electrical fire except carelessness fire. Joint/contact badness is the one of the main cause of electrical fire. Furthermore, power relays which are used in electric panel board, motor control center and automation controller, are main element of automation system in the industry field. Overload, voltage unbalance and open-phase due to joint/contact badness of terminal make electric accidents or electrical fires. In order to prevent joint/contact badness of terminal, this paper proposes a sensing circuit of chattering, tracking, arc current, voltage unbalance and open-phase etc. Some experimental tests of the proposed apparatus confirm practicality and validity of the theoretical results.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.11
• /
• pp.2101-2106
• /
• 2009

The power system state estimation and prediction are very important for operation. Because that accidents of the Power system are the cause that many devices and etc are damaged. Currently, almost every power systems have 2nd,3rd back-upsystem for prevention of accident. But prevention of accident by miss-operation, due to operator or miss data, has not acounter plan. Because, we need to estimate the power system for correcting miss data and preventing miss operation by operator. We suggest algorithm for integrity of power system network data.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2013.11a
• /
• pp.945-947
• /
• 2013

입력 모듈의 결함경향성을 결정하는 결함 예측 모델 연구들은 대부분 훈련 데이터 집합을 사용하는 감독형 모델에 관련된 것들이었다. 하지만 과거 데이터 집합이 없거나 현재 프로젝트 성격이 다른 경우는 비감독형 모델이 필요하며, 이들에 관한 연구들은 모델 구축의 어려움 때문에 극소수 존재한다. 본 논문에서는 대표적인 클러스터링 알고리즘들을 사용한 비감독형 모델들을 제작하여, 기존 모델들이 많이 사용한 K-means 모델과 나머지 모델들의 성능을 비교하였다.

• Electrical & Electronic Materials
• /
• v.11 no.11
• /
• pp.1-8
• /
• 1998

This paper presents an arificial neuro-fuzzy technique based prtial discharge (PD) pattern classifier to power system application. This may require a complicated analysis method employ -ing an experts system due to very complex progressing discharge form under exter-nal stress. After referring briefly to the developments of artificical neural network based PD measurements, the paper outlines how the introduction of new emerging technology has resulted in the design of a number of PD diagnostic systems for practical applicaton of residual lifetime prediction. The appropriate PD data base structure and selection of learning data size of PD pattern based on fractal dimentsional and 3-D PD-normalization, extraction of relevant characteristic fea-ture of PD recognition are discussed. Some practical aspects encountered with unknown stress in the neuro-fuzzy techniques based real time PD recognition are also addressed.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2021.05a
• /
• pp.459-462
• /
• 2021

본 연구는 반도체 제조 공정중 발생하는 센서 데이터를 활용하여 딥러닝기반으로 불량을 예측하는 모델을 제안한다. 반도체 공장에서는 FDC((Fault Detection and Classification)라는 불량을 예측하는 시스템이 있지만, 공정의 복잡도가 높고 센서의 종류가 많아 공정 관리자가 모든 센서의 기준을 설정 및 관리하는데 한계가 있다. 이를 해결하기 위해 공정 설비의 센서 데이터를 딥러닝을 활용하여 학습시켜 센서 기준정보로 임계치를 제공하고, 가공중 발생하는 센서 데이터가 입력되면 정상 여부를 판정하는 모델을 제안한다.