• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.191-197
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• 2006

This paper deals with a fault detection system design for nuclear steam generator water level control system. We expressed the nonlinear properties of the steam generator level system as a T-S fuzzy system with time varying uncertain parameters. We design a residual generator using a left coprime factorization of the T-S fuzzy model and a fault detection filter in order to improve the fault detection performance. We demonstrate the efficiency of the suggested design method via many computer simulations.

• The Transactions of the Korean Institute of Power Electronics
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• v.18 no.3
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• pp.205-210
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• 2013

This paper presents a novel hall sensor fault detection and fault-tolerant control method for a high-speed permanent magnet synchronous motor (PMSM) drive system. A phase locked loop (PLL) type position estimator is used with a conventional interpolation based rotor position estimator to reduce position errors due to misalignment of hall sensors. The expected trigger time of hall sensor's output is used for detecting hall sensor fault condition and the PLL type position estimator is reconfigured for fault-tolerant control at the hall sensor fault condition. The proposed method can minimize current ripples during the transition from sensored control using hall sensors to sensorless control. Experimental results have been proposed to prove the validity of the proposed method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2619-2627
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• 2009

The ground fault is occupying 70% among the total number of faults in ungrounded distribution power system. When the ground fault occurs in ungrounded system, the fault current is so small that it is hard to detect. But fault handling is very important because to continue power supply during fault conditions may cause the fault spreading and the distribution device in trouble. This paper presents the fault line detection method by using GPT signal detecting zero sequence voltage, and the fault section detection method by detecting whether GPT signal is disappeared or not during shifting normally open switch, which is connecting switch between distribution lines with open state in order to restore the outage area under emergency situation, and during isolating each section one by one which belongs to the fault line. This method is efficient because there is no whole power interruption during the fault section detection, and it is possible to perform both the fault section detection and the service restoration for the outage area at the same time, and it can apply to various distribution system configuration. Program for the fault restoration was developed applying proposed method, and it has been validated by applying to the pilot project of distribution automation system in Vietnam which has the ungrounded distribution system.

• Proceedings of the KSR Conference
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• 2000.05a
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• pp.336-343
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• 2000

This paper proposes the failsafe control logic. which has applied to the voting on the TMR system by using FPGA The self-detection circuit is also designed for detecting a characteristic of fault at TMR system. The fault producing in the self-detection system is largely classified among an intermittent fault, a transient fault and a permanent fault. If it is happened to the permanent fault, the system can be failed. Therefore, it is designed the logic circuit which is not transferred the permanent fault to the system after shut off output. The control logic of the Fail Safe proposed in the paper is required for a circuit integrate of device to minimize the failure happened. Therefore, it makes to design FPGA with modeling of VHDL. The circuit of the Fail Safe of TMR system is able to apply to nuclear system, rail-way system, aerospace and aircraft system which is required for high reliability.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.29 no.11 s.242
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• pp.1220-1228
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• 2005

Fault detection and diagnostic (FDD) systems have the potential to reduce equipment downtime, service costs, and utility costs. In this study, model based algorithm and fuzzy algorithm were used to detect and diagnose various fault at System multi-air conditioner. various fault include the Refrigerant Low charging, Fouling of Indoor Heat Exchanger, Fouling of Outdoor Heat Exchanger A experimental verification was conducted in the 6HP System multi-air conditioner on an 8-floor building. Test results showed diagnosis result about 78 $\~$ 90$\%$ for given faults. This Study lays the foundation fur future work on develope the real-time fault detection and diagnosis system for the System multi-air conditioner.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.2
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• pp.77-82
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• 2001

This paper deals with a fault detection algorithm for linear descriptor systems via unknown input PI observer. An unknown input PI observer is presented and its realization conditions is proposed by using the rank condition of system matrices. From the characteristics of unknown input PI observer, the states of system with unknown inputs are estimated and the occurrences of fault are detected, and its magnitudes are estimated easily by using integrated output estimation error under the step faults. Finally, a numerical example is given to verify the effectiveness of the proposed fault detection algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.192-197
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• 2020

Multivariate processes, such as large scale power plants or chemical processes are operated in very hazardous environment, which can lead to significant human and material losses if a fault occurs. On-line monitoring technology, therefore, is essential to detect system faults. In this paper, the ICA-based fault detection method is conducted using three different multivariate process data. Fault detection procedure based on ICA is divided into off-line and on-line processes. The off-line process determines a threshold for fault detection by using the obtained dataset when the system is normal. And the on-line process computes statistics of query vectors measured in real-time. The fault is detected by comparing computed statistics and previously defined threshold. For comparison, the PCA-based fault detection method is also implemented in this paper. Experimental results show that the ICA-based fault detection method detects the system faults earlier and better than the PCA-based method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.115-123
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• 2021

A lot of sensor and control signals is generated by an industrial controller and related internet-of-things in discrete manufacturing system. The acquired signals are such records indicating whether several process operations have been correctly conducted or not in the system, therefore they are usually composed of binary numbers. For example, once a certain sensor turns on, the corresponding value is changed from 0 to 1, and it means the process is finished the previous operation and ready to conduct next operation. If an actuator starts to move, the corresponding value is changed from 0 to 1 and it indicates the corresponding operation is been conducting. Because traditional fault detection approaches are generally conducted with analog sensor signals and the signals show stationary during normal operation states, it is not simple to identify whether the manufacturing process works properly via conventional fault detection methods. However, digital control signals collected from a programmable logic controller continuously vary during normal process operation in order to show inherent sequence information which indicates the conducting operation tasks. Therefore, in this research, it is proposed to a recurrent neural network-based fault detection approach for considering sequential patterns in normal states of the manufacturing process. Using the constructed long short-term memory based fault detection, it is possible to predict the next control signals and detect faulty states by compared the predicted and real control signals in real-time. We validated and verified the proposed fault detection methods using digital control signals which are collected from a laser marking process, and the method provide good detection performance only using binary values.

• Aerospace Engineering and Technology
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• v.6 no.2
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• pp.21-28
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• 2007

The goal of this paper is to represent a technique of fault detection for the control surface as a base research of the fault tolerant control system for safety improvement of UAV. The real-time system identification based on the recursive Fourier Transform was implemented for the fault detection of the control surface and verified through the HILS and flight test. The failures of the control surface are detected by comparing the control derivatives in fault condition with the normal condition. As a result from the flight test, we have confirmed that the control derivatives of fault condition less than normal condition.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.4
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• pp.254-260
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• 2000

This paper deals with the development of fault tolerant control for a nonlinear boiler system with noise and disturbance. The MCMBPC(Multivariable Constrained Model Based Predictive Control) is adopted for the control of the specific boiler turbin model. The fault detection and diagnosis are accomplished with the Kalman filter and two bias estimators. Once a fault is detected, two Bias estimators are driven to estimate the fault and to discriminate Process fault and sensor fault. In this paper, a fault tolerant control scheme combining MCMBPC with a fault compensation method based on the bias estimator is proposed. The proposed scheme has been applied to the nonlinear boiler system and shown a satisfactory performance through some simulations.