• Proceedings of the KSME Conference
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• 2004.04a
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• pp.774-779
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• 2004

This paper describes the fault detection and failsafe logic to be used in the Electronic Stability Program (ESP). The Aim of this paper is prevention of erroneous control in the ESP. This paper introduces the fault detection logic and evaluation of residual signals. Failsafe logic consist of four redundant sub-models and they can be used for the detection of faults in each sensor (yaw rate, lateral acceleration, steering wheel angle). We presents two mathematical residual generation method ; one is the method by the average value, and the other is the method by the minimum value of the each residual. We verify a failsafe logic using vehicle test results, also we compare vehicle model based simulation results with test vehicle results.

• Proceedings of the KSME Conference
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• 2008.11b
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• pp.2070-2075
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• 2008

Identification of steady-state is the first step in developing a fault detection and diagnosis (FDD) system. In a complete FDD system, the steady-state detector will be included as a module in a self-learning algorithm which enables the working system's reference model to "tune" itself to its particular installation. In this study, a steady-state detector of a residential air conditioner based on moving windows was designed. Seven representing measurements were selected as key features for steady-state detection. The optimized moving window size and the feature thresholds was suggested through startup transient test and no-fault steady-state test. Performance of the steady-state detector was verified during indoor load change test. From the research, the general methodology to design a moving window steady-state detector was provided for vapor compression applications.

• International Journal of Air-Conditioning and Refrigeration
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• v.16 no.4
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• pp.137-144
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• 2008

This study deals with on-line fault detection and diagnosis(FDD) for heat exchangers of a variable speed refrigeration system(VSRS) based on current information. The current residual which is the difference between real detected current from current sensors and estimated current from no fault model was utilized to diagnose faults of the heat exchangers. Comparing to the conventional FDD of constant refrigeration system based on temperature and pressure information, the suggested FDD method shows better robustness to the VSRS which has a feedback control loop. Moreover the suggested method can be expected more precise and faster diagnosis of faults about heat exchangers. Throughout some experiments, the validity of the method was verified.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.1171-1176
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• 2004

To develop and improve a switchgear, the prediction of the pressure rising within the switchgear is very important. This study investigates the pressure rising characteristics of switchgear in order to evaluate the result of arc fault test. The pressure rising time at the four points of measurement calculated by CFD is well accord with the experimental results. The maximum pressure within the switchgear estimated by CFD is about 1.0bar, the pressure from experiment is 0.7 bar. The results of this study are able to be used to improve the performance of existing switchgear and to develop a new type switchgear.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.388-392
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• 2009

Operation of LNG (Liquefied Natural Gas) plants requires an effective maintenance strategy. To this end, the long-term and short-term trend of faults, such as mechanical and electrical troubles, should be identified so as to take proactive approach for ensuring the smooth and productive operation. However, it is not an easy task to predict the fault trend in LNG plants. Many variables and unexpected conditions make it quite difficult for the facility manager to be well prepared for future faulty conditions. This paper presents a model to predict the fault trend in a LNG plant. ARIMA (Auto-Regressive Integrated Moving Average) model is combined with Wavelet Transform to enhance the prediction capability of the proposed model. Test results show the potential of the proposed model for the preventive maintenance strategy.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.822-829
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• 2022

According to existing study into the remote fault diagnosis procedure, the current diagnostic approach has an imperfect decision model, which only supports communication in a close distance. An Internet of Things (IoT)-based remote fault diagnostic approach for wind power equipment is created to address this issue and expand the communication distance of fault diagnosis. Specifically, a decision model for active power coordination is built with the mechanical energy storage of power generation equipment with a remote diagnosis mode set by decision tree algorithms. These models help calculate the failure frequency of bearings in power generation equipment, summarize the characteristics of failure types and detect the operation status of wind power equipment through IoT. In addition, they can also generate the point inspection data and evaluate the equipment status. The findings demonstrate that the average communication distances of the designed remote diagnosis method and the other two remote diagnosis methods are 587.46 m, 435.61 m, and 454.32 m, respectively, indicating its application value.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.4
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• pp.565-578
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• 2016

A probabilistic based systems approach is addressed in this study for the reliability prediction of solid rocket motors (SRM). To achieve this goal, quantitative Failure Modes, Effects and Criticality Analysis (FMECA) approach is employed to determine the reliability of components, which are integrated into the Fault Tree Analysis (FTA) to obtain the system reliability. The quantitative FMECA is implemented by burden and capability approach when they are available. Otherwise, the semi-quantitative FMECA is taken using the failure rate handbook. Among the many failure modes in the SRM, four most important problems are chosen to illustrate the burden and capability approach, which are the rupture, fracture of the case, and leak due to the jointed bolt and O-ring seal failure. Four algorithms are employed to determine the failure probability of these problems, and compared with those by the Monte Carlo Simulation as well as the commercial code NESSUS for verification. Overall, the study offers a comprehensive treatment of the reliability practice for the SRM development, and may be useful across the wide range of propulsion systems in the aerospace community.

• Journal of Power Electronics
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• v.10 no.3
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• pp.262-269
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• 2010

This paper proposes a practical design for a Cascaded H-Bridge Multilevel (CHBM) inverter based on Power Electronics Building Blocks (PEBB) and high performance control to improve current control and increase fault tolerance. It is shown that the expansion and modularization characteristics of the CHBM inverter are improved since the individual inverter modules operate more independently, when using the PEBB concept. It is also shown that the performance of current control can be improved with voltage delay compensation and the fault tolerance can be increased by using unbalance three-phase control. The proposed design and control methods are described in detail and the validity of the proposed system is verified experimentally in various industrial fields.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.10
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• pp.1466-1473
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• 2017

Generally, electrical torque in synchronous generator is balanced with the rotor mechanical torque under steady-state condition. Thus, the synchronous generator rotor rotates at constant speed. However, under fault condition, the electrical torque output is suddenly decreased and the sum of both torques does not remain constant. If the mechanical torque is not decreased at the same time, the generator rotor would accelerate. Therefore, this accelerating generator rotates at different speeds with respect to other generators in the power system. This phenomena is called as Out-of-Step (OOS). In this paper, we presented a certain two-step type quadrilateral OOS relay setting, which is applicable in actual field, and examined the validity of its setting value with OOS simulation conditions due to delayed fault clearing in transmission line. In order to conduct the study of OOS relay characteristics, we checked the impedance locus and generator output characteristics under the various delayed fault clearing conditions. Moreover, we proposed a countermeasure for avoiding the misoperation of OOS relay during the stable swing by modifying the setting values.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.57-64
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• 2017

In order to reduce damages to major railroad components, which have the potential to cause interruptions to railroad services and safety accidents and to generate unnecessary maintenance costs, the development of rolling stock maintenance technology is switching from preventive maintenance based on the inspection period to predictive maintenance technology, led by advanced countries. Furthermore, to enhance trust in accordance with the speedup of system and reduce maintenances cost simultaneously, the demand for fault diagnosis and prognostic health management technology is increasing. The objective of this paper is to propose a highly reliable learning model using various machine learning algorithms that can be applied to critical rolling stock components. This paper presents a model for railway rolling stock component fault diagnosis and conducts a mechanical failure diagnosis of motor components by applying the machine learning technique in order to ensure efficient maintenance support along with a data preprocessing plan for component fault diagnosis. This paper first defines a failure diagnosis model for rolling stock components. Function-based algorithms ANFIS and SMO were used as machine learning techniques for generating the failure diagnosis model. Two tree-based algorithms, RadomForest and CART, were also employed. In order to evaluate the performance of the algorithms to be used for diagnosing failures in motors as a critical railroad component, an experiment was carried out on 2 data sets with different classes (includes 6 classes and 3 class levels). According to the results of the experiment, the random forest algorithm, a tree-based machine learning technique, showed the best performance.