• Aerospace Engineering and Technology
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• v.6 no.2
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• pp.150-156
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• 2007

In this paper, we focus on the fault detection and isolation(FDI) method for inertial navigation system with three two-degree of freedom dynamically tuned gyros(DTG) on orthogonal sensor configuration. we propose the FDI method which can detect the fault of each DTGs rather than the fault of each sensing axis. The proposed FDI method is separated into two FDI modules according to the fault magnitude in order to improve the reliability of fault detection information. For large fault detection, only instantaneous DTG measurement is used to detect a fault DTG within a short time. For small fault detection, the integrated value of DTG measurements are used to detect a fault DTG. It takes a more time to detect a fault but it serves more reliable fault detection information. Using the proposed FDI method with consideration of DTG fault characteristic, we could find out a fault DTG successfully.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.87-87
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• 2000

In this paper, an integrated design of fault detection, diagnosis and reconfigurable control tot multi-input and multi-output system is proposed. It is based on the interacting multiple model estimation algorithm, which is one of the most cost-effective adaptive estimation techniques for systems involving structural and/or parametric changes. This research focuses on the method to recover the performance of a system with failed actuators by switching plant models and controllers appropriately. The proposed scheme is applied to a fault tolerant control design for flight control system.

• Proceedings of the KIPE Conference
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• 2000.07a
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• pp.172-176
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• 2000

The paper presents the fault detection and isolation of two DC-DC converters parallel operation by ZCT method. Two experimental prototype converters were designed and implemented for evaluation of fault tolerant system. The experimental results show that fault detection and isolation circuit works very well.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.416-424
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• 2016

In this study, automatic detection of stator winding inter-turn short circuit fault (SWISCFs) in surface-mounted permanent magnet synchronous motors (SPMSMs) and automatic classification of fault severity via a pattern recognition system (PRS) are presented. In the case of a stator short circuit fault, performance losses become an important issue for SPMSMs. To detect stator winding short circuit faults automatically and to estimate the severity of the fault, an artificial neural network (ANN)-based PRS was used. It was found that the amplitude of the third harmonic of the current was the most distinctive characteristic for detecting the short circuit fault ratio of the SPMSM. To validate the proposed method, both simulation results and experimental results are presented.

• Journal of the Korea Institute of Military Science and Technology
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• v.3 no.2
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• pp.140-148
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• 2000

This paper presents a fault detection and isolation(FDI) method based on Generalized Likelihood Ratio(GLR) test for the tightly coupled SDINS/CPS system. The GLR test is known to have the capability of detecting an assumed change while estimating its occurrence time and magnitude, and isolating the changing part. Once a fault is detected even if we don't know if the fault occurrs at either INS or GPS, multi-hypothesized GLR scheme performs the fault isolation between INS and GPS, and find which satellite malfunctions. However, in the INS faulty case, it turned out to fail to accomodate the fault isolation between accelerometer and gyroscope due to the coupling effects and a poor observability of the system. Hence, to isolate the INS fault, it needs to change the attitude of the vehicle resulting in enhancing the degree of observability.

• IE interfaces
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• v.20 no.3
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• pp.288-297
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• 2007

Success of semiconductor/LCD industry depends on its yield and quality of product. For the purpose, FDC (Fault Detection and Classification) system is used to diagnose fault state in main manufacturing processes by monitoring time series data collected by equipment sensors which represent various conditions of the equipment. The data set is segmented at the start and end of each product lot processing by a trigger event module. However, in practice, segmented sensor data usually have the features of data asynchronization such as different start points, end points, and data lengths. Due to the asynchronization problem, false alarm (type I error) and missed alarm (type II error) occur frequently. In this paper, we propose a robust process fault detection system by integrating a process event detection method and a similarity measuring method based on dynamic time warping algorithm. An experiment shows that the proposed system is able to recognize abnormal condition correctly under the asynchronous data situation.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.1012-1014
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• 1997

This paper introduces wavelets and shows that they may be efficient and useful for the detection of general faults in power system. The wavelet transform of a signal consists in measuring the "similarity" between the signal and a set of translated and scaled versions of a "mother wavelet". The "mother wavelet" is a chosen fast decaying oscillation function. A number of mother wavelet for signal analysis have been proposed and some of them are in use in fault detection. However, the performance of fault detection depend on used mother wavelet. In the present paper a comparative evaluation of different mother wavelets for low impedance fault detection is performed. The discussion is focused in well-known mother wavelet based wavelet transform. Several families of wavelets are used to analyse transient earth fault signals in a 345kV model system as generated by EMTP.

• 한국신재생에너지학회:학술대회논문집
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• 2008.05a
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• pp.408-411
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• 2008

In this paper, a new DC ground fault detection system is proposed, which is suitable for photovoltaic power generation systems. The proposed ground fault systems is superposition of divide resistance and detection circuit. The proposed system has the characteristics of a simplified structure, reduced cost and volume compared with those of the conventional ground fault system for DC source. The operation principle of the proposed systems is described and verified by simulation result.

• Journal of Advanced Marine Engineering and Technology
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• v.30 no.2
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• pp.247-252
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• 2006

Almost ship monitoring systems are event driven alarm system which warn only when the measurement value is over or under set point. These kinds of system cannot warn until signal is growing to abnormal state that the signal is over or under the set point. therefore cannot play a role for preventive maintenance system. This paper proposes fault diagnosis method which is able to diagnose and forecast the fault from present operating condition by analyzing monitored signals with present ship monitoring system without any additional sensors. By analyzing the data with high correlation coefficient(CC), correlation level of interactive data can be defined. Knowledge base of abnormal detection can be built by referring level of CC(Fault Detection CC. FDCC) to detect abnormal data among monitored data from monitoring system and knowledge base of diagnosis built by referring CC among interactive data for related machine each other to diagnose fault part.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.281-286
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• 2005

Almost ship monitoring systems are event driven alarm system which warn only when the measurement value is over or under set point. These kinds of system cannot warn while signal is growing to abnormal state until the signal is over or under the set point and cannot play a role for preventive maintenance system. This paper proposes fault diagnosis method which is able to diagnose and forecast the fault from present operating condition by analyzing monitored signals with present ship monitoring system without additional sensors. By analyzing this data having high correlation coefficient(CC), correlation level of interactive data can be understood. Knowledge base of abnormal detection can be built by referring level of CC(Fault Detection CC, FDCC) to detect abnormal data among monitored data from monitoring system and knowledge base of diagnosis built by referring CC among interactive data for related machine each other to diagnose fault part.