• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.883-887
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• 2002

We should maintain the maximum operation capacity for production facilities and find properly out the fault of each equipment rapidly in order to decrease a loss caused by its failure. The acoustic and vibration signals of a machine always carry the dynamic information of the machine. These signals are very useful fur the feature extraction and fault diagnosis. We performed a fundamental study which develops a system of fault diagnosis for a pump. We experimented vibrations by acceleration sensors and noises by microphones, compared and analysed for normal products, artificially deformed products. We tried to search a change of the dynamic signals according to machine malfunctions and analyse the type of deformation or failure. The results showed that acoustic signals as well as vibration signals can be used as a simple method lot a detection of machine malfunction or fault diagnosis.

• Industrial Engineering and Management Systems
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• v.14 no.4
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• pp.379-391
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• 2015

Several risk assessment techniques have been presented and investigated in previous research, focusing mainly on the failure mode and effect analysis (FMEA). FMEA can be employed to determine where failures can occur within industrial systems and to assess the impact of such failures. This research proposes a novel methodology for hazard analysis and risk assessments that integrates FMEA with the bow-tie model. The proposed method has been applied and evaluated in a real industrial process, illustrating the effectiveness of the proposed method. Specifically, the bowtie diagram of the critical equipment in the adopted plant in the case study was built. Safety critical barriers are identified and each of these is assigned to industrial process with an individual responsible. The detection rating to the failure mode and the values of risk priority number (RPN) are calculated. The analysis shows the high values of RPN are 500 and 490 in this process. A global corrective actions are suggested to improve the RPN measure. Further managerial insights have been provided.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.572-573
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• 2021

By optimizing the development of IoT-type thermal image-based photovoltaic fault detection equipment and interworking with drones using a drone with an intelligent path movement function, real-time analysis of the acquired image data facilitates fault reading of solar power plants. , design a system that can read out the failure of a solar panel using the image subtraction analysis technique and the presentation of the basic technology that can improve the power generation rate of the solar power plant and make an efficient maintenance model.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.1
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• pp.132-137
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• 2006

The second part of paper related rotor failure is to evaluate that the axial magnetic flux measurement could be used as a tool of the condition monitoring system for the induction motor and to develope the diagnostic algorithm for the various fault in the electric motors. The magnetic leakage flux signal is captured by the flux coil located at the end of motor without the disturbance of the operation. And the signal is analyzed both time and frequency domain to detect the failure of the motor. Specific signature can be described in tin and frequency domain for each fault of the motor. The experimental test found that the rotor failures - broken rotor bar, broken end ing and rotor eccentricity, could be detected from the spectrum with high resolution. The method of detecting the rotor fault was found by analysing the specific frequency and the sideband of the rotor bar pass frequency from axial leakage flux spectrum. In addition the optimal flux coil and measuring equipment for the axial leakage flux measurement was verified and the diagnostic method for the detection of the rotor related failure was developed.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.367-374
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• 2022

The purpose of this study was to propose a deep learning algorithm that applies to the fault diagnosis of fuel pumps and purifiers of autonomous ships. A deep learning algorithm reflecting the time dependence of the measured signal was configured, and the failure pattern was trained using the vibration signal, measured in the equipment's regular operation and failure state. Considering the sequential time-dependence of deterioration implied in the vibration signal, this study adopts Conv1D with sliding window computation for fault detection. The time dependence was also reflected, by transferring the measured signal from two-dimensional to three-dimensional. Additionally, the optimal values of the hyper-parameters of the Conv1D model were determined, using the grid search technique. Finally, the results show that the proposed data preprocessing method as well as the Conv1D model, can reflect the sequential dependency between the fault and its effect on the measured signal, and appropriately perform anomaly as well as failure detection, of the equipment chosen for application.

• Journal of the Korea Society of Computer and Information
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• v.3 no.2
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• pp.147-154
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• 1998

As technical systems in building are being developed, the processes and systems get more difficult for the average operator to understand. When operating a complex facility, it is beneficial in equipment management to provide the operator with tools which can help in dicision making for recovery from a failure of the system. The main object of the study is to develop real-time automatic fault detection and diagnosis system for optimal operation of IBS building.

• Proceedings of the KIEE Conference
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• 2001.04a
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• pp.19-21
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• 2001

Induction motors are a critical component of many industrial machines and are frequently integrated in commercial equipment. The many 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 is used for induction motor fault diagnosis. This method analyzes the motor's supply current, since this diagnoses the motor's condition. The diagnostic system is constructed by using LabVIEW of National Instruments.

• 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.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2172-2174
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• 2001

Induction motors are a critical component of many industrial machines and are frequently integrated in commercial equipment. The many 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 is used for induction motor fault diagnosis. This method analyzes the motor's supply current, since this diagnoses the motor's condition. The diagnostic system is constructed by using LabVIEW of National Instruments.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.65.1-65
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• 2001

Induction motors are a critical component of many industrial machines and are frequently integrated in commercial equipment. The many 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 is used for induction motor fault diagnosis. This method analyzes the motor´s supply current, since this diagnoses the motor´s condition. The diagnostic system is constructed by using LabVIEW of National Instruments.