• The Transactions of the Korean Institute of Power Electronics
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• v.16 no.5
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• pp.457-465
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• 2011

The fault analysis and detecting algorithm for a 3 phase diode rectifier is proposed. The 3 phase dioderectifier is used for the AC power rectifier of the PWM inverter. The input power or diode faults cause theripples of the DC voltage, degradation of the control performance and life shortening of the DC link capacitor.In this paper, the ripple of the DC voltage is mathematically analyzed for the earth fault of input power andopen circuit fault of the diode, respectively. The fault detection and type of fault can be obtained by comparingthe average DC voltage and the instant DC voltage which is sampled with 6 times of grid frequency. Theproposed method can be easily applicable and doesn't require additional circuit. The experimental and simulationresults are presented to verify the validity of the proposed method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.2
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• pp.185-195
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• 2018

As machines have been automated in the field of industries in recent years, it is a paramount importance to manage and maintain the automation machines. When a fault occurs in sensors attached to the machine, the machine may malfunction and further, a huge damage will be caused in the process line. To prevent the situation, the fault of sensors should be monitored, diagnosed and classified in a proper way. In the paper, we propose a sensor fault detection scheme based on SVM and CNN to detect and classify typical sensor errors such as erratic, drift, hard-over, spike, and stuck faults. Time-domain statistical features are utilized for the learning and testing in the proposed scheme, and the genetic algorithm is utilized to select the subset of optimal features. To classify multiple sensor faults, a multi-layer SVM is utilized, and ensemble technique is used for CNN. As a result, the SVM that utilizes a subset of features selected by the genetic algorithm provides better performance than the SVM that utilizes all the features. However, the performance of CNN is superior to that of the SVM.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2687-2698
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• 2020

Most of the machine learning-based intrusion detection tools developed for Industrial Control Systems (ICS) are trained on network packet captures, and they rely on monitoring network layer traffic alone for intrusion detection. This approach produces weak intrusion detection systems, as ICS cyber-attacks have a real and significant impact on the process variables. A limited number of researchers consider integrating process measurements. However, in complex systems, process variable changes could result from different combinations of abnormal occurrences. This paper examines recent advances in intrusion detection algorithms, their limitations, challenges and the status of their application in critical infrastructures. We also introduce the discussion on the similarities and conflicts observed in the development of machine learning tools and techniques for fault diagnosis and cybersecurity in the protection of complex systems and the need to establish a clear difference between them. As a case study, we discuss special characteristics in nuclear power control systems and the factors that constraint the direct integration of security algorithms. Moreover, we discuss data reliability issues and present references and direct URL to recent open-source data repositories to aid researchers in developing data-driven ICS intrusion detection systems.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.5
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• pp.29-37
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• 2002

This paper presents the diagnostic method for fault prevention in metal clad switchgear(MCS) through comparison of signals before and after detecting the partial discharge using electromagnetic detection technique. Electromagnetic waves detected by antennas of the inside and outside of MCS are analyzed and compared by frequency spectrum analysis method which can estimate an insulation abnormality and normality of MCS. As a result of the experiment by the proposed method, we can detect the insulation abnormality as partial discharge in MCS and these results can be applied to preventive diagnosis of MCS.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.215-220
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• 2013

Recently, death number of heart attack in the world is increasing rapidly. Therefore, to solve these problem, it is trend that is making mandatory automatic defibrillator AED establishment to airport, school, at home. However, AED use in an emergency or equipment failure caused malfunctions if equipped with AED may even become obsolete. In this paper, in order to improve this problem, AED Simulator using the fuzzy simulation technique in comparison to existing methods Tilt ambient temperature conditions and in consideration of the conditions, self-diagnostics, error detection at the time to determine whether the development of intelligent simulation. Moreover, in this paper, it proved that fuzzy AED Simulation improved fault detection probability results 30% more than conventional method.

• Nuclear Engineering and Technology
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• v.53 no.2
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• pp.532-548
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• 2021

Nuclear power plants contain several monitoring systems that can identify the in-vessel phenomena of a severe accident (SA). Though a lot of analysis and research is carried out on SA, right from the development of the nuclear industry, not all the possible circumstances are taken into consideration. Therefore, to improve the efficacy of the safety of nuclear power plants, additional analytical studies are needed that can directly monitor severe accident phenomena. This paper presents an interacting multiple model (IMM) based fault detection and diagnosis (FDD) approach for the identification of in-vessel phenomena to provide the accident propagation information using reactor vessel (RV) out-wall temperature distribution during severe accidents in a nuclear power plant. The estimation of wall temperature is treated as a state estimation problem where the time-varying wall temperature is estimated using IMM employing three multiple models for temperature evolution. From the estimated RV out-wall temperature and rate of temperature, the in-vessel phenomena are identified such as core meltdown, corium relocation, reactor vessel damage, reflooding, etc. We tested the proposed method with five different types of SA scenarios and the results show that the proposed method has estimated the outer wall temperature with good accuracy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.437-441
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• 1999

In this paper, a fault diagnosis method using unknown-input proportional integral (PI) observers including the magnitude of actuator failures is proposed. It is shown that actuator failures are detected and isolated perfectly by monitoring the integrated error between the actual output and the estimated output using an unknown-input PI observer. Also in presence of complex actuator and sensor failures, these failures are detected and isolated by multiple unknown-input PI observers perfectly.

• Proceedings of the KIEE Conference
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• 2001.07c
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• pp.1676-1678
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• 2001

In general, power demand is on an increasing trend as industries have made rapid strides. Power transformer is the most important equipment in substation for this reason. Transformer trobles go with blackout, expensive repair costs and huge economic losses. Therefore it is important to find the quick detection of incipient fault for the least losses. There have been gas, partial discharge, temperature, OLTC, fan and pump diagnosis for preventive techniques by present. Specially gas analysis has been adapted for a long time and proved as confident method. In this paper, we analysed the fault causes of used power transformer. The insulation faults was occupied 40% of inquired 152 faults from 1991 to 2000. This study presents the developed algorithm and expert system for finding abnormal status within transformer. We used the Element Expert tool developed Neuron DATA Inc.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.627-629
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• 1999

This paper presents a new FDI scheme based on dynamic fuzzy model(DFM) for the nonlinear system. The dynamic behavior of a nonlinear system is represented by a set of local linear models. The parameters of the DFM are identified in on-line and aggregated to generate a residual vector by the approximate reasoning. The neural network classifer learns the relationship between the residual vector and fault type and used both for the detection and isolation of process faults We apply the proposed FDI scheme to the FDI system design for a two-tank system and show the usefulness of the proposed scheme.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.20-27
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• 2000

A chatter vibration and a workpiece burn are the main phenomena to be monitored in modern grinding processes. This study describes a trouble diagnosis of the cylindrical plunge grinding process using the power and acoustic emission (AE) signals. The raw signals of the power and the AE occurred during the grinding operation were sampled and analyzed to determine the relationship between each fault and change of signals. A neural network that has a high success rate of the fault detection was used. Furthermore, an analysis on the influence of parameters to the chatter vibration and the grinding burn was conducted.