• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.537-543
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• 2003

Condition monitoring and fault diagnosis of machines are gaining importance in the industry because of the need to increase reliability and to decrease possible loss of production due to machine breakdown. By comparing the vibration signals of a machine running in normal and faulty conditions, detection of faults like mass unbalance, shaft misalignment and bearing defects is possible. This paper presents a novel approach for applying the fault diagnosis of rotating machinery. To detect multiple faults in rotating machinery, a feature selection method and support vector machine (SVM) based multi-class classifier are constructed and used in the faults diagnosis. The results in experiments prove that fault types can be diagnosed by the above method.

• International Journal of Aeronautical and Space Sciences
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• v.7 no.1
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• pp.73-83
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• 2006

In this paper, a redundancy management system for aircraft is studied, and fault detection and isolation algorithms of inertial sensor system are proposed. Contrary to the conventional aircraft systems, UAV system cannot allow triple or quadruple hardware redundancy due to the limitations on space and weight. In the UAV system with dual sensors, it is very difficult to identify the faulty sensor. Also, conventional fault detection and isolation (FDI) method cannot isolate multiple faults in a triple redundancy system. In this paper, two FDI techniques are proposed. First, hardware based FDI technique is proposed, which combines a parity equation approach with a wavelet based technique. Second, analytic FDI technique based on the Kalman filter is proposed, which is a model-based FDI method utilizing the threshold value and the confirmation time. To provide the reference value for detecting the fault, residuals are calculated using the extended Kalman filter. To verify the effectiveness of the proposed FDI methods, numerical simulations are performed.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.19 no.4
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• pp.533-546
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• 2021

Large earthquakes with (M_W > ~ 6) result in ground shaking, surface ruptures, and permanent deformation with displacement. The earthquakes would damage important facilities and infrastructure such as large industrial establishments, nuclear power plants, and waste disposal sites. In particular, earthquake ruptures associated with large earthquakes can affect geological and engineered barriers such as deep geological repositories that are used for storing hazardous radioactive wastes. Earthquake-driven faults and surface ruptures exhibit various fault zone structural characteristics such as direction of earthquake propagation and rupture and asymmetric displacement patterns. Therefore, estimating the respect distances and hazardous areas has been challenging. We propose that considering multiple parameters, such as fault types, distribution, scale, activity, linkage patterns, damage zones, and respect distances, enable accurate identification of the sites for deep geological repositories and important facilities. This information would enable earthquake hazard assessment and lower earthquake-resulted hazards in potential earthquake-prone areas.

• Journal of the Korean earth science society
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• v.26 no.5
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• pp.436-442
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• 2005

We have performed multiple geophysical surveys comprised of gravity, magnetic and resistivity methods at the Yangsan fault zone which runs through the Eonyang area, the eastern part of Kyeongsang in southeast Korea. The gravity and magnetic data provide information about geological structures. Furthermore, sections of electrical resistivity show the sharp contrast of electrical resistivity distribution across the fault zone. Since the fractured zone tends to be more conductive than fresh host rocks, the electrical resistivity survey is effective in determining the detailed structure of the fault zone. We have made gravity measurements at a total of 71 points alongside two profiles across the fault zone, and carried out an electrical resistivity survey with a dipole-dipole array at the same location using 40m dipole length. In addition, we have analyzed the aeromagnetic data on the corresponding area. The multiple geophysical properties appear to be abruptly changed in electrical resistivity, gravity and aeromagneticclearly show the different appearance across the fault zone. The fault is identified by its sub vertical attitude which is well known in the Yangsan fault zone. We have also confirmed that the magnitude of the response of the fault is much larger in the southern part of the survey area than the northern area. These results most likely to provide basic information for the further studies about the physical properties and the structures at the Yangsan fault.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.12
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• pp.637-644
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• 2004

This paper proposes an expert system with the knowledge learning capability which can enhance the safety and effectiveness of substation operation in the automated substation as well as existing substation by inferring multiple events such as main transformer fault, busbar fault and main transformer work schedule under multiple inference mode and multiple objective mode and by considering totally the switch status and the main transformer operating constraints. Especially inference mode includes the local minimum tree search method and pattern recognition method to enhance the performance of real-time bus reconfiguration strategy. The inference engine of the expert system consists of intuitive inferencing part and logical inferencing part. The intuitive inferencing part offers the control strategy corresponding to the event which is most similar to the real event by searching based on a minimum distance classification method of pattern recognition methods. On the other hand, logical inferencing part makes real-time control strategy using real-time mode(best-first search method) when the intuitive inferencing is failed. Also, it builds up a knowledge base or appends a new knowledge to the knowledge base using pattern learning function. The expert system has main transformer fault, main transformer maintenance work and bus fault processing function. It is implemented as computer language, Visual C++ which has a dynamic programming function for implementing of inference engine and a MFC function for implementing of MMI. Finally, it's accuracy and effectiveness is proved by several event simulation works for a typical substation.

• The Journal of Engineering Geology
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• v.24 no.3
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• pp.397-409
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• 2014

The appearance of faults during tunnel construction is often difficult to predict in terms of strike, dip, scale, and strength, even though this information is essential in determining the strength of the surrounding rock mass. However, the strength and rock mass classification of fault zones are generally determined empirically on the construction site. In this study, 109 specimens were collected from fault of nine area throughout Korea, and direct shear tests were conducted and the particle distribution was analyzed to better characterize the fault zones. Six multiple regression models were established, using 97 of the specimens, to analyze the correlation between the shear strengths and weight rations of these fault materials. A verification of the six models, using the remaining 12 specimens, shows that in all of the models the coefficient of determination yielded $R^2{\geq}0.60$, with two models yielding $R^2{\geq}0.69$. These results provide useful information for determining the shear strength of fault materials in future studies.

• Journal of Electrical Engineering and Technology
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• v.3 no.4
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• pp.484-490
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• 2008

In this paper, an integrated fault section detection and isolation strategy is proposed based on the application of the Distribution Automation System(DAS) utilizing advanced IT and communication technologies. The Feeder Remote Terminal Unit(FRTU) has been widely used to collect data in the Korean distribution system. The achieved data is adopted in this method for detecting multiple fault types. Especially in the case of single phase-to-ground fault, the fault section is detected by comparison of the zero-sequence current phase angle. The test results have verified the effectiveness of the proposed method in a radial distribution system through extensive simulations in Matlab/Simulink. Furthermore, a communication-based demo system identical to the simulation model has been developed, and it can be applied as an online monitoring and control program for fault section detection and isolation.

• Transactions on Electrical and Electronic Materials
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• v.6 no.6
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• pp.262-271
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• 2005

In order to achieve timely and accurate fault detection of plasma etching process, neural network based time series modeling has been applied to reactive ion etching (RIE) using two different in-situ plasma-monitoring sensors called optical emission spectroscopy (OES) and residual gas analyzer (RGA). Four different subsystems of RIE (such as RF power, chamber pressure, and two gas flows) were considered as potential sources of fault, and multiple degrees of faults were tested. OES and RGA data were simultaneously collected while the etching of benzocyclobutene (BCB) in a $SF_6/O_2$ plasma was taking place. To simulate established TSNNs as incipient fault detectors, each TSNN was trained to learn the parameters at t, t+T, ... , and t+4T. This prediction scheme could effectively compensate run-time-delay (RTD) caused by data preprocessing and computation. Satisfying results are presented in this paper, and it turned out that OES is more sensitive to RF power and RGA is to chamber pressure and gas flows. Therefore, the combination of these two sensors is recommended for better fault detection, and they show a potential to the applications of not only incipient fault detection but also incipient real-time diagnosis.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.129-143
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• 2021

This paper proposes actuator fault detection and adaptive fault-tolerant control algorithms using performance index and human-like learning for longitudinal autonomous vehicles. Conventional longitudinal controller for autonomous driving consists of supervisory, upper level and lower level controllers. In this paper, feedback control law and PID control algorithm have been used for upper level and lower level controllers, respectively. For actuator fault-tolerant control, adaptive rule has been designed using the gradient descent method with estimated coefficients. In order to adjust the control parameter used for determination of adaptation gain, human-like learning algorithm has been designed based on perceptron learning method using control errors and control parameter. It is designed that the learning algorithm determines current control parameter by saving it in memory and updating based on the cost function-based gradient descent method. Based on the updated control parameter, the longitudinal acceleration has been computed adaptively using feedback law for actuator fault-tolerant control. The finite window-based performance index has been designed for detection and evaluation of actuator performance degradation using control error.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.11
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• pp.1117-1124
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• 2011

An integrated fault detection and isolation method is proposed in this paper. The main objective of this paper is development fault detection, isolation and diagnosis algorithm based on the DKF (Decentralized Kalman Filter) and the bank of IMM (Interacting Multiple Model) filters using penalty scalar for both partial and total faults and the outlier detection algorithm for preventing false alarm also included. The proposed FDI (Fault Detection and Isolation) scheme is developed in four phases. In the first phase, the outlier detection filter is designed to prevent false alarm as a pre-filter. In the second phases, two local filters and master filter are designed to detect sensor faults. In the third phases, the proposed FDI scheme checks sensor residual to isolate sensor faults and 11 EKFs actuator fault models are designed to detect wherever actuator faults occur. In the last phases, four filters are designed to identify the fault type which is either the total fault or partial fault. The developed scheme can deal with not only sensor and actuator faults, but also preventing false alarm. An important feature of the proposed FDI scheme can decreases fault isolation time and figure out not only fault detection and isolation but also fault type identification. To verify the proposed FDI algorithm performance, the Simulator is also developed under the Matlab/Simulink environment.