• Journal of Institute of Control, Robotics and Systems
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• v.11 no.10
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• pp.880-887
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• 2005

This paper presents a fault diagnosis(detection and isolation) approach for the Ammunition Transport Automation system(ATAS). Due to limited time and information available during its cyclic operation, the on-line fault detection algorithm consists of sequential test logics referring to the normal states, which can be considered as a kind of expert system. If a failure were detected, the off-line isolation algorithm finds the fault location through trained ART2 neural network. By the results of simulations and some on-line field test, it has been shown that the presented approach is effective enough and applicable to related automation systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.204-210
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• 1998

This paper discusses an on-line fuzzy dynamic model(FDM) identification of nonlinear processes for the design of fuzzy model based fault detection and isolation(FDI). The dynamic behavior of a nonlinear process is represented by a fuzzy aggregation of a set of local linear models. The identification is divided into two procedures. The first is the off-line identification of membership function. The second is the on-line identification of the local linear models. Then, we propose a residual generation scheme based on the parameters of local linear models and show that the scheme can be used for the design of FDI

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.60 no.3
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• pp.114-119
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• 2011

Large Power transformer is a complex and critical component of power plant and consists of cellulosic paper, insulation oil, core, coil etc. Insulation materials of transformer and related equipment break down to liberate dissolved gas due to corona, partial discharge, pyrolysis or thermal decomposition. The dissolved gas kinds can be related to the type of electrical faults, and the rate of gas generation can indicate the severity of the fault. The identities of gases being generated are using very useful to decide the condition of transformation status. Therefore dissolved gas analysis is one of the best condition monitoring methods for power transformer. Also, on-line multi-gas analyzer has been developed and installed to monitor the condition of critical transformers. Rogers method, IEC method, key gas method and Duval Triangle method are used to failure diagnosis typically, and those methods are using the ratio or kinds of dissolved gas to evaluate the condition of transformer. This paper analyzes the reliability of transformer diagnostic methods considering actual dissolved gas concentration. Fault diagnosis is performed based on the dissolved gas of five transformers which experienced various fault respectively in the field, and the diagnosis result is compared with the actual off-line fault analysis. In this comparison result, Diagnostic methods using dissolved gas ratio like Rogers method, IEC method are sometimes fall outside the ratio code and no diagnosis but Duval triangle method and Key gas method is correct comparatively.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.11a
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• pp.653-657
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• 2004

In this study, PD(partial discharge) signals which occurrs at stator coil of traction Motor are acquired. these data are used for classifying the PD sources. W(Neural Network) has recently applied to classify the PB pattern. The PD data are used for the learning process to classify PD sources. The PD data come from normal specimen and defective specimens such as internal void discharges, slot discharges and surface discharges. PD distribution parameters are calculated from a set of the data, which is used to realize diagnostic algorithm. NN which applies distribution parameters is useful to classify the PD patterns of defective sources generating in stator coil of traction motor.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.1522-1528
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• 2007

Electric Power cable is the apparatus that receives electric power from the Korea Electric Power Corporation and supplies electric power to electric train and annex facilities of each railway station. With substantial ripple effect during power blackout accidents, such power blackout accidents must be coped with by discriminating the status of insulation deterioration of electric power cable in advance. Discrimination of insulation deterioration of the electric power cable is normally executed while the power is disconnected and it is very difficult to discover, at early stage, the insulation deterioration of the power cable in operational state since the duration of inspection is limited. This research aims to consider method of diagnosing the insulation deterioration of electric power cable in On-Line state rather than diagnosis in Off-Line state in order to secure reliability of power supply by reducing duration of power blackout (accidental blackout and blackout during works) and by seeking reduction in equipment and manpower used in diagnosis of deterioration through prevention of the accident itself prior to occurrence through early restoration of accident due to insulation deterioration of the electric power cable and assessment of performance of the cable under operation.

• Journal of Electrical Engineering and Technology
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• v.3 no.3
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• pp.422-429
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• 2008

This paper presents compact low frequency ultra-wide band (UWB) sensor design and study of the partial discharge diagnosis by sensing electromagnetic pulse emitted from the partial discharge source with the newly designed UWB sensor. In this study, we designed a new type of compact low frequency UWB sensor based on microstrip antenna technology to detect both the low frequency and high frequency band of the partial discharge signal. Experiments of offline PD testing on medium voltage (22.9kV) underground cable mention the comparative results with the traditional HFCT as a reference sensor in the laboratory. In the series of comparative tests, the calibration signal injection test provided with the conventional IEC 60270 method and high voltage injection testing are included.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.11
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• pp.968-973
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• 2000

Fault diagnosis plays an important role in the performance and safe operation of many modern engineering plants. This paper investigates the problem of fault detection using neural networks in dynamic systems. A general framework for constructing a nonlinear fault detection scheme for nonlinear dynamic systems containing modeling uncertaintly is proposed. The main idea behind the proposed approach is to monitor the physical system with an off -line learning neural network and then to approximate the upper and lower thresholds of acceleration of the nominal system with the model-based threshold(ThMB) method, The performance of the proposed fault detection scheme is investigated through simulations of a pendulum with uncertainty.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.4
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• pp.105-110
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• 1998

In this study, statistical and neural network methods were used to recognize the cutting tool states. This system employed the tool dynamometer and cutting force signals which are processed from the tool dynamometer sensor using linear discriminent function. To learn the necessary input/output mapping for turning operation diagnosis, the weights and thresholds of the neural network were adjusted according to the error back propagation method during off-line training. The cutting conditions, cutting force ratios and statistical values(standard deviation, coefficient of variation) attained from the cutting force signals were used as the inputs to the neural network. Through the suggested neural network a cutting tool states may be successfully diagnosed.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.16-21
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• 2008

Aero-engine, as one kind of rotating machinery with complex structure and high rotating speed, has complicated vibration faults. Therefore, condition monitoring and fault diagnosis system is very important for airplane security. In this paper, a vibration data acquisition and intelligent fault diagnosis system is introduced. First, the vibration data acquisition part is described in detail. This part consists of hardware acquisition modules and software analysis modules which can realize real-time data acquisition and analysis, off-line data analysis, trend analysis, fault simulation and graphical result display. The acquisition vibration data are prepared for the following intelligent fault diagnosis. Secondly, two advanced artificial intelligent(AI) methods, mapping-based and rule-based, are discussed. One is artificial neural network(ANN) which is an ideal tool for aero-engine fault diagnosis and has strong ability to learn complex nonlinear functions. The other is data mining, another AI method, has advantages of discovering knowledge from massive data and automatically extracting diagnostic rules. Thirdly, lots of historical data are used for training the ANN and extracting rules by data mining. Then, real-time data are input into the trained ANN for mapping-based fault diagnosis. At the same time, extracted rules are revised by expert experience and used for rule-based fault diagnosis. From the results of the experiments, the conclusion is obvious that both the two AI methods are effective on aero-engine vibration fault diagnosis, while each of them has its individual quality. The whole system can be developed in local vibration monitoring and real-time fault diagnosis for aero-engine.

• Tuberculosis and Respiratory Diseases
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• v.84 no.3
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• pp.237-244
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• 2021

Background: The emergence of drug-resistant tuberculosis (TB), is a major menace to cast off TB worldwide. Line probe assay (LPA; GenoType MTBDRplus ver. 2) and Xpert MTB/RIF assays are two rapid molecular TB detection/diagnostic tests. To compare the performance of LPA and Xpert MTB/RIF assay for early diagnosis of rifampicin-resistant (RR) TB in acid-fast bacillus (AFB) smear-positive and negative sputum samples. Methods: A total 576 presumptive AFB patients were selected and subjected to AFB microscopy, Xpert MTB/RIF assay and recent version of LPA (GenoType MTBDRplus assay version 2) tests directly on sputum samples. Results were compared with phenotypic culture and drug susceptibility testing (DST). DNA sequencing was performed with rpoB gene for samples with discordant rifampicin susceptibility results. Results: Among culture-positive samples, Xpert MTB/RIF assay detected Mycobacterium tuberculosis (Mtb) in 97.3% (364/374) of AFB smear-positive samples and 76.5% (13/17) among smear-negative samples, and the corresponding values for LPA test (valid results with Mtb control band) were 97.9% (366/374) and 58.8% (10/17), respectively. For detection of RR among Mtb positive molecular results, the sensitivity of Xpert MTB/RIF assay and LPA (after resolving discordant phenotypic DST results with DNA sequencing) were found to be 96% and 99%, respectively. Whereas, specificity of both test for detecting RR were found to be 99%. Conclusion: We conclude that although Xpert MTB/RIF assay is comparatively superior to LPA in detecting Mtb among AFB smear-negative pulmonary TB. However, both tests are equally efficient in early diagnosis of AFB smear-positive presumptive RR-TB patients.