• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.204-210
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• 1998

This paper discusses an on-line fuzzy dynamic model(FDM) identification of nonlinear processes for the design of fuzzy model based fault detection and isolation(FDI). The dynamic behavior of a nonlinear process is represented by a fuzzy aggregation of a set of local linear models. The identification is divided into two procedures. The first is the off-line identification of membership function. The second is the on-line identification of the local linear models. Then, we propose a residual generation scheme based on the parameters of local linear models and show that the scheme can be used for the design of FDI

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.2
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• pp.146-153
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• 2011

Model based machine condition diagnosis methods are generally using a normal and many failure models which need sufficient data to train the models. However, data, especially for failure modes of interest, is very hard to get in real applications. So their industrial applications are either severely limited or impossible when the failure models cannot be trained. In this paper, continuous hidden Markov model(CHMM) with only a normal model has been suggested as a very promising machine condition diagnosis method which can be easily used for industrial applications. Generally hidden Markov model also uses many pattern models to recognize specific patterns and the recognition results of CHMM show the likelihood trend of models. By observing this likelihood trend of a normal model, it is possible to detect failures. This method has been successively applied to arc weld defect diagnosis. The result shows CHMM's big potential as a machine condition monitoring method.

• Design & Manufacturing
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• v.16 no.4
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• pp.52-59
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• 2022

Currently, Korea is an aging society and is expected to become a super-aged society in about four years. X-ray devices are widely used for early diagnosis in hospitals, and many X-ray technologies are being developed. The development of X-ray device technology is important, but it is also important to increase the reliability of the device through accurate data management. Sensor nodes such as temperature, voltage, and current of the diagnosis device may malfunction or transmit inaccurate data due to various causes such as failure or power outage. Therefore, in this study, the temperature, tube voltage, and tube current data related to each sensor and detection circuit of the diagnostic X-ray imaging device were measured and analyzed. Based on QC data, device failure prediction and diagnosis algorithms were designed and performed. The fault diagnosis algorithm can configure a simulator capable of setting user parameter values, displaying sensor output graphs, and displaying signs of sensor abnormalities, and can check the detection results when each sensor is operating normally and when the sensor is abnormal. It is judged that efficient device management and diagnosis is possible because it monitors abnormal data values (temperature, voltage, current) in real time and automatically diagnoses failures by feeding back the abnormal values detected at each stage. Although this algorithm cannot predict all failures related to temperature, voltage, and current of diagnostic X-ray imaging devices, it can detect temperature rise, bouncing values, device physical limits, input/output values, and radiation-related anomalies. exposure. If a value exceeding the maximum variation value of each data occurs, it is judged that it will be possible to check and respond in preparation for device failure. If a device's sensor fails, unexpected accidents may occur, increasing costs and risks, and regular maintenance cannot cope with all errors or failures. Therefore, since real-time maintenance through continuous data monitoring is possible, reliability improvement, maintenance cost reduction, and efficient management of equipment are expected to be possible.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.7
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• pp.813-822
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• 2012

Abnormal operating sounds radiated from a moving transfer robot in LCD (liquid crystal display) product lines have been used for the fault detection line of a robot instead of other source signals such as vibrations, acoustic emissions, and electrical signals. Its advantage as a source signal makes it possible to monitor the status of multiple faults by using only a microphone, despite a relatively low sensitivity. The wavelet packet transform for feature extraction and the artificial neural network for fault classification are employed. It can be observed that the abnormal operating sound is sufficiently useful as a source signal for the fault diagnosis of mechanical components as well as other source signals.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.16 no.2
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• pp.1-11
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• 2008

Increasing demand for improved reliability and survivability of mission-critical systems is driving the development of health monitoring and Automated Contingency Management (ACM) systems. An ACM system is expected to adapt autonomously to fault conditions with the goal of still achieving mission objectives by allowing some degradation in system performance within permissible limits. ACM performance depends on supporting technologies like sensors and anomaly detection, diagnostic/prognostic and reasoning algorithms. This paper presents the development of a generic prototype test bench software framework for developing and validating ACM systems for advanced propulsion systems called the Propulsion ACM (PACM) Test Bench. The architecture has been implemented for a Monopropellant Propulsion System (MPS) to demonstrate the validity of the approach. A Simulink model of the MPS has been developed along with a fault injection module. It has been shown that the ACM system is capable of mitigating the failures by searching for an optimal strategy. Furthermore, the concepts of Validation and Verification (V&V) of such systems are introduced with relevant examples.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.2075-2076
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• 2011

The stator winding faults diagnosis technique based on MCSA is as follows. Firstly, collecting the 3 phase motor currents, that signal is transformed by (d-q transform, $i_d$, $i_q$). Park's vector pattern, the circle that is down by d-q transformed currents($i_d$, $i_q$). The circle is widely used for stator winding faults detection. The current distortion ratio(DR), defined by the ratio of max-axis and min-axis of ellipse of Park's vector's pattern. In this study, distortion ratio of Park's vector pattern is suggested for Auto diagnosis of stator winding short fault and usefulness of distortion ratio is verified through simulation using LabVIEW program.

• Journal of the Korean Operations Research and Management Science Society
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• v.16 no.2
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• pp.148-158
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• 1991

The Built-In-Test (BIT) system is an integrated subsystem for the determination of the health status of any primary system. The BIT consists of hardware and software installations directed at performance of the functions of fault detection, diagnosis and isolation, as well as primary system record failure information. Evaluation of the difinitions appropriate to the BIT system, including system characteristics and parameters, is important to an understanding of system functions. The object of this paper is to present general definitions of the BIT diagnosis parameters and a semiquantiative evaluation method for BIT systems. Finally, two case studies for actual problem solutions are included.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.573-575
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• 2005

In the steel industry, a steel melting electric arc furnace is used to produce both carbon and alloy steels. Steel scrap which is charged into the furnace is heated by means of electric arc between graphite electrodes and the scrap. In this melting process, current is supplied to the furnace through HCC(high current cable) which connect the furnace and transformer. Four HCCs are assigned to each phase in our system to divide the current. Since a sudden cable breaking result in the shutdown of melting process, an aging detection of HCC is very important for both an improvement of productivity and cost reduction. In this paper, the aging of the HCC is estimated by using the current ratio between four HCCs.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.257-260
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• 1997

Bluetooth is the most promising network paradigm which ca open the new area in the information technology. Especially, bluetooth can link all the electrical products and PCs(Personal Computer) to cellular phone or PDA. In this paper, the data from ECU which are gathered by scanner are communicated between tow PCs using the bluethooth modules. The acquired data are ECU's self diagnosis signal and sensor output signal. Self diagnosis signals are very important to check the ECU's state and sensor output signals. Using these data, the possibility of wireless communication with ECU is developed and verified. Protocol stack of bluetooth is L2CAP through HCI and wireless communication software of ECU's signal is developed using VC++ in Windows 98 environment.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.3
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• pp.115-118
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• 2006

In the steel industry, a steel melting electric arc furnace is used to produce both carbon and alloy steels. Steel scrap which is charged into the furnace is heated by means of electric arc between graphite electrodes and the scrap. In this melting process, current is supplied to the furnace through HCC(high current cable) which connect the furnace and transformer. Four HCCs are assigned to each phase in our system to divide the current. Since a sudden cable breaking result in the shutdown of melting process, an aging detection of HCC is very important for both an improvement of productivity and cost reduction. In this paper, the aging of the HCC is estimated by using the current ratio between four HCCs.