• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.960-968
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• 2005

The high cost in maintaining complex manufacturing process makes it necessary to enhance an efficient maintenance system. For the effective maintenance of manufacturing process, precise fault diagnosis should be performed and an appropriate maintenance action should be executed. This paper suggests an intelligent fault diagnosis system using hybrid data mining. In this system, the rules for the fault diagnosis are generated by hybrid decision tree/genetic algorithm and the most effective maintenance action is selected by decision network and AHP. To verify the proposed intelligent fault diagnosis system, we compared the accuracy of the hybrid decision tree/genetic algorithm with one of the general decision tree learning algorithm(C4.5) by data collected from a coil-spring manufacturing process.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.11C
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• pp.75-84
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• 2001

In this paper, we propose an on-line FDD(Fault Detection and Diagnosis) system based on the three layer feed-forward neural network which is trained by the back-propagation teaming algorithm. We implement the on-line fault detection and diagnosis system by Visual C++ and Visual Basic. The proposed FDD system is applied to an air handling unit in operation. Experimental results show the high performance of our system in the task of fault detection and diagnosis.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2222-2224
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• 2002

Induction motors are critical components of many industrial machines and are frequently integrated in commercial equipment. The heavy economical losses and the deterioration of system reliability might be caused by the failure of induction motors in industrial field. Based on the reliability and cost competitiveness of driving system (motors), the faults detection and diagnosis of system is considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method are used for induction motor fault diagnosis. This method analyzes the motors supply current. since this diagnoses faults of the motor. The diagnostic algorithm is based on the artificial neural network, and the diagnosis system is programmed by using LabVIEW and MATLAB.

• Journal of the Korea Safety Management & Science
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• v.6 no.3
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• pp.303-309
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• 2004

Many system devices for fluoroscopic and general X-ray studies in diagnostic radiographic system have been being changed from analog mode to digital mode. In addition, among diagnostic imaging and radiologic examinations, fluoroscopic studies that requires functional diagnosis is being widely used. The video recording method of fluoroscopic studies has been useful in functional image diagnosis and dynamic image observation, but the utility of its image quality is being reduced because of limitation in setting play segments of the video player, inconvenience of play, difficulties in preserving reproduced images, the change of image quality, etc. In order to complement these shortages, it is necessary to facilitate access to patient diagnosis information such as storing, editing and sharing functional diagnosis images in response to the trend of the digitalization of digital radiographic & fluoroscopic system(DRF). Thus this study designed and implemented a device of storing functional dynamic images real time using a computer rather than existing video recording, aiming at contribution to functional image diagnosis.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.4
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• pp.215-222
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• 2009

Human voice reacts very sensitively to human's minute physical condition. For instance, human voice disorders affect patients profoundly especially in the case of Parkinson's disease. Acoustic tools such as MDVP, can function as an equipment that measures various voice in different objects. Many different approaches have been applied for analyzing the voice disorders for diagnosis of Parkinson's disease. According to the voice data of suspected Parkinson's patients from UCI Machine Learning Repository, it is reported to have 23 people with Parkinson's disease and 8 healthy people. Applying Mahalanobis Taguchi System (MTS) for diagnosis of Parkinson's disease, the correct diagnosis performance is compared to previous research results.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.10
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• pp.89-97
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• 2003

The faults diagnosis of machine tool, which is controlled by CNC(Computer Numerical Control) and PLC(Programmable Logic Controller), is generally based on ladder diagram of PLC because sequential controls for CNC and servo motor are mostly processed in PLC. However, when fault is occurred, a searching of logical relationship for fault reasons is required a lot of diagnosis experiences and times because PLC program has step structure. In this paper, FDS(Fault Diagnosis System) is developed and implemented to machine tool with open architecture controller in order to find the reason of fault fast and correctly. The diagnosed reasons for fault are remote serviced on web through developed RSS(Remote Service System). The operationability and usefulness of developed system are evaluated on specially manufactured machine tool with open architecture CNC. The results of this research can be the model of remote monitoring and fault diagnosis system of machine tool with open architecture CNC.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.05a
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• pp.1005-1010
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• 2000

Internet service has widely used in office automation such as, manufacturing management purchase, and material resource. Nowadays, factory automation and shop floor control system including CAD/CAM department need a web based monitoring and diagnosis to achieve global collaboration and tole-service. This raper deal with design of web based remote monitoring and diagnosis system which concerned with open architecture controller for machine tool.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.05a
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• pp.643-646
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• 2000

As many industrial systems become more complex, it becomes extremely difficult to diagnose the cause of failures. This paper presents a failure diagnosis approach based on discrete event system theory. In particular, the approach is a hybrid of event-based and state-based ones leading to a simpler failure diagnoser with supervisory control capability. The design procedure is presented along with a pump-valve system as an example.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.39-52
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• 2002

Research Direction The significant research direction in mechanical fault diagnosis area: Theorles and approaches for fault feature extracting and fault classification. Identification Complicated fault generating mechanism and its model Intelligent fault diagnosis system (including the expert system and network based remote diagnosis system) One of the Key Points： Fault feature extracting techniques based on (modern) signal processing(omitted)

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.425-430
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• 2008

To verify the system performance of portable AE leak diagnosis system which can measure with moving conditions, AE activities such as RMS voltage level, AE signal trend, leak rate degree according to AE database, FFT spectrum were measured during operation on total 11 valves of the secondary system in nuclear power plant. AE activities were recorded and analyzed from various operating conditions including different temperature, type of valve, pressure difference, valve size and fluid. The results of this field study are utilized to select the type of sensors, the frequency band for filtering and thereby to improve the signal-to-noise ratio for diagnosis for diagnosis or monitoring of valves in operation. As the final result of application study above, portable type leak diagnosis system by AE was developed. The outcome of the study can be definitely applied as a means of the diagnosis or monitoring system for energy saving and prevention of accident for power plant valve. The purpose of this study is to verify availability of the acoustic emission in-situ monitoring method to the internal leak and operating conditions of the major valves at nuclear power plants. In this study, acoustic emission tests are performed when the pressurized temperature water and steam flowed through glove valve(main steam dump valve) and check valve(main steam outlet pump check valve) on the normal size of 12 and 18 ". The valve internal leak monitoring system for practical field was designed. The acoustic emission method was applied to the valves at the site, and the background noise was measured for the abnormal plant condition. To improve the reliability, a judgment of leak on the system was used various factors which are AE parameters, trend analysis, frequency analysis, voltage analysis and amplitude analysis of acoustic signal emitted from the valve operating condition internal leak.