• 한국추진공학회:학술대회논문집
• /
• 한국추진공학회 2008년 영문 학술대회
• /
• pp.16-21
• /
• 2008

Aero-engine, as one kind of rotating machinery with complex structure and high rotating speed, has complicated vibration faults. Therefore, condition monitoring and fault diagnosis system is very important for airplane security. In this paper, a vibration data acquisition and intelligent fault diagnosis system is introduced. First, the vibration data acquisition part is described in detail. This part consists of hardware acquisition modules and software analysis modules which can realize real-time data acquisition and analysis, off-line data analysis, trend analysis, fault simulation and graphical result display. The acquisition vibration data are prepared for the following intelligent fault diagnosis. Secondly, two advanced artificial intelligent(AI) methods, mapping-based and rule-based, are discussed. One is artificial neural network(ANN) which is an ideal tool for aero-engine fault diagnosis and has strong ability to learn complex nonlinear functions. The other is data mining, another AI method, has advantages of discovering knowledge from massive data and automatically extracting diagnostic rules. Thirdly, lots of historical data are used for training the ANN and extracting rules by data mining. Then, real-time data are input into the trained ANN for mapping-based fault diagnosis. At the same time, extracted rules are revised by expert experience and used for rule-based fault diagnosis. From the results of the experiments, the conclusion is obvious that both the two AI methods are effective on aero-engine vibration fault diagnosis, while each of them has its individual quality. The whole system can be developed in local vibration monitoring and real-time fault diagnosis for aero-engine.

• 대한방사선기술학회지:방사선기술과학
• /
• 제21권1호
• /
• pp.35-39
• /
• 1998

We analyzed image factors to determine the characteristic factors that need for intelligent replenishment system of the auto film processor. We processed the serial 300 sheets of radiographic films of chest phantom without replenishment of developing and fixation replenisher. We took the digital data by using film digitizer which scaned the films and automatically summed up the pixel values of the films. We analyzed characteristic curves, average gradients and relative speeds of individual film using densitometer and step densitometry. We also evaluated the pH of developer, fixer, and washer fluid with digital pH meter. Fixer residual rate and washing effect were measured by densitometer using the reagent methods. There was no significant reduction of the digital density numbers of the serial films without replenishment of developer and fixer. The average gradients were gradually decreased by 0.02 and relative speeds were also gradually decreased by 6.96% relative to initial standard step-densitometric measurement. The pHs of developer and fixer were reflected the inactivation of each fluid. The fixer residual rates and washing effects after processing each 25 sheets of films were in the normal range. We suggest that the digital data are not reliable due to limitation of the hardware and software of the film digitizer. We conclude that average gradient and relative speed which mean the film's contrast and sensitivity respectively are reliable factors for determining the need for the replenishment of the auto film processor. We need more study of simpler equations and programming for more intelligent replenishment system of the auto film processor.

• 한국공작기계학회논문집
• /
• 제12권4호
• /
• pp.29-35
• /
• 2003

The rotating machineries always generate harmonic frequencies of their own rotating speed, and increment of vibration amplitude affects to the equipments which connected to the vibrational source and causes industrial calamities. The life cycle of equipments can be extended and damages to the human beings could be prevented by identifying the cause of malfunctions through prediction of the increment of vibration and records of vibrational history. In this study, therefore, diagnostic expert algorithm for the centrifugal pump is developed by integrating fuzzy inference method and signal processing techniques. And the validity of the developed diagnostic system is examined via various computer simulations.

• 한국컴퓨터정보학회논문지
• /
• 제15권1호
• /
• pp.23-30
• /
• 2010

의료 분야의 정보화와 다양화로 인해 한의학 분야에서도 지능화된 서비스를 제공해주는 온톨로지 기반의 지능형 의료 시스템에 관한 연구가 진행되고 있다. 지능형 의료 시스템은 온톨로지를 이용하여 복잡한 의료지식 및 개인의 의료정보등을구조화함으로써진단을과학화시키고보다나은의료서비스를제공하게해준다. 본 논문에서는 온톨로지를 이용하여 기본적인 의학 데이터, 진단 시 발생되는 임상데이터, 개인의 신체정보와 같은 세 가지 지식을 표현하여 온톨로지로 구축함으로써 개인 맞춤형 진단을 내리는데 중요한 데이터로 활용한다. 특히, 한의학진단에서는 환자 개인의 병증과 체질 등에 따라 상이한 진단 및 처방이 내려질 수 있기 때문에 개개인의 신체정보 및 질병 정보를 이용하여 사용자의 상황에 맞는 맞춤형의 진단 및 처방 서비스를 제공 해주는 지능형 진단보조시스템이 유용하다. 따라서 본 논문에서는 환자 개개인에게 맞춤형의 진단 서비스를 제공하기 위한 방법으로 개인의 신체정보 및 질병정보를 이용하여 한의학 온톨로지를 구축하고, 추론을 통해 진단을 내리는 한의학 진단보조시스템을 구현하였다.

• 지능정보연구
• /
• 제4권1호
• /
• pp.115-131
• /
• 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.

• 한국생산제조학회지
• /
• 제22권2호
• /
• pp.292-297
• /
• 2013

Nowadays, a strategy of the self-optimizing machining process is an imperative approach to improve the product quality and increase productivity of manufacturing systems. This paper presents a concept of self-optimizing forming system that allows the forming system automatically to adjust the forming parameters online for guarantee the product quality and avoiding the machine stop. An intelligent monitoring system that has the functions of observation, evaluation and diagnostic is developed to evaluate the pully quality during forming process. Any abnormal variation of forming machining parameters could be detected and adjusted by an intelligent control system aiming to maintain the machining stability and the desired product quality. This approach is being practiced on the pully forming machine for evaluating the efficiency of the proposed strategy.

• 반도체디스플레이기술학회지
• /
• 제18권4호
• /
• pp.87-91
• /
• 2019

The diagnosis of the failure for the existing electrical facilities was based on regular preventive maintenance, but this preventive maintenance was limited in preventing a lot of cost loss and sudden system failure. To overcome these shortcomings, fault prediction and diagnostic techniques are critical to increasing system reliability by monitoring electrical installations in real time and detecting abnormal conditions in the facility early. As the performance and quality deterioration problem occurs frequently due to the increase in the number of users of the motor pump, the purpose is to build an intelligent control system that can control the motor pump to maximize the performance and to improve the quality and reliability. To this end, a vibration sensor, temperature sensor, pressure sensor, and low water level sensor are used to detect vibrations, temperatures, pressures, and low water levels that can occur in the motor pump, and to build a system that can identify and diagnose information to users in real time.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제9권3호
• /
• pp.178-184
• /
• 2009

An efficient fault diagnosis system is needed for industry because it can optimize the resources management and improve the performance of the system. In this study, a fault diagnostic system is proposed for rotating machine using wavelet packet transform (WPT) and elman neural network (ENN) techniques. In most fault diagnosis for mechanical systems, WPT is a well-known signal processing technique for fault detection and identification. In previous work, WPT can improve the continuous wavelet transform (CWT) used over a longer computing time and huge operand. It can also solve the frequency-band disagreement by discrete wavelet transform (DWT) only breaking up the approximation version. In the experimental work, the extracted features from the WPT are used as inputs in an Elman neural network. The results show that the scheme can reliably diagnose four different conditions and can be considered as an improvement of previous works in this field.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제5권4호
• /
• pp.347-352
• /
• 2005

PD(Partial discharges) are small electrical sparks that occur within the electric insulation of cables, transformers and windings on motors. PD analysis is a proactive diagnostic approach that uses PD measurements to evaluate the integrity of this equipment. Recently, several diagnostic algorithms for classifying the type of PD and locating the defect position have been developed. In this work, a new PD recognition system is proposed, which utilizes approximate coefficients of wavelet transform as a feature vector, furthermore, introduces bank of Elman networks to recognize the various PD phenomena. In order to verify the performance of the proposed scheme, it is applied to the simulated PD data.

• 한국지능정보시스템학회:학술대회논문집
• /
• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
• /
• pp.508-510
• /
• 2001

Adopting the method of user weighting fuzzy mathematics, the author accomplished the subject title “Study on Expert System of Chicken\`s Common Diseases Diagnostics”, which could properly diagnose 30 kinds of chicken\`s common diseases and the accordance rate reached 80% verified through 244 disease cases. On the basis of the accomplishment, the multimedia technology was adopted further more to establish a system, which integrated with the input, display, query, and processing of sound, picture and text etc., combined with the previous chicken disease diagnostic expert system, make the output information of computer more rich and comprehensive, and the accordance rate of disease diagnosis could be improved. The system consists of database, knowledge base, graphics and picture base. This system is easy to operate and interface of which is vivid and intuitive. It could output diagnostic result and prescribe rapidly, so that, such a system is not only adapted to large, medium chicken farm but also to grass-roots veterinary station for developing health care and disease diagnosing. It is sure that the system could have side prospect of application.