• Journal of Power Electronics
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• v.16 no.3
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• pp.1097-1109
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• 2016

Fault detection and isolation are related to system monitoring, identifying when a fault has occurred, and determining the type of fault and its location. Fault detection is utilized to determine whether a problem has occurred within a certain channel or area of operation. Fault detection and diagnosis have become increasingly important for many technical processes in the development of safe and efficient advanced systems for supervision. This paper presents an integrated technique for fault diagnosis and classification for open- and short-circuit faults in three-phase inverter circuits. Discrete wavelet transform and principal component analysis are utilized to detect the discontinuity in currents caused by a fault. The features of fault diagnosis are then extracted. A fault dictionary is used to acquire details about transistor faults and the corresponding fault identification. Fault classification is performed with a fuzzy logic system and relevance vector machine (RVM). The proposed model is incorporated with a set of optimization techniques, namely, evolutionary particle swarm optimization (EPSO) and cuckoo search optimization (CSO), to improve fault detection. The combination of optimization techniques with classification techniques is analyzed. Experimental results confirm that the combination of CSO with RVM yields better results than the combinations of CSO with fuzzy logic system, EPSO with RVM, and EPSO with fuzzy logic system.

• Tuberculosis and Respiratory Diseases
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• v.39 no.1
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• pp.1-6
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• 1992

The diagnosis of tuberculosis is usually established using staining and culturing techniques. Fluorescent stains have improved the sensitivity of direct microscopy. Improved culture media coupled with radiometric means of detecting early mycobacterial growth have shortened the time needed for cultural diagnosis. Rapid immunodiagnostic techniques based on the detection of mycobacterial antigen or of antibodies to theses antigens have not, however, come into widespread clinical use. The DNA or RNA hybridization tests with labeled specific probes which have been described so far are not sensitive enough to be used for clinical speicimens without prior culturing. The advent of the polymerase chain reaction (PCR) has opened new possibilities for diagnosis of microbial infections. This technique has already been applied to a number of microorganisms. In the field of mycobacteria the PCR has been used to identify and to detect DNAs extracted from various mycobacteria. However, despite the extraordinary enthusiasm surrounding this technique and the considerable investiment, PCR has not emerged from the developmental "trenches" in the passed several years. It may be a considerable lenth of time before clinical microbiology laboratories become PCR playgrounds because many details remain to be worked out.

• Korean Journal of Agricultural Science
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• v.38 no.1
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• pp.45-50
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• 2011

This study was conducted to investigate the farm situation about bovine leukemia virus(BLV) infection that greatly influence productivity in dairy cattle and compare the accuracy of diagnosis for BLV infection between PCR and ELISA techniques. Blood samples of 193 heads from 5 herds in Chungnam and Chungbuk area were used to analyze BLV gene and serum, and the results were obtained as follows. The amplified BLV gene in dairy cattle by PCR technique resulted in 226 bp, 596 bp and 434 bp, respectively, for gag, pol and env, which were well amplified. The infection rates of BLV virus diagnosed by PCR and ELISA techniques ranged from 80.55 to 100% and from 22.22 to 86.95%, respectively, and the infection rates among 5 herds were significantly different in both methods (P<0.05). Further, the average infection rates of 5 herds were 87.05 and 63.21%, respectively, for PCR and ELISA techniques. Kappa statistics for examining consistency of diagnosis by PCR and ELISA techniques showed 0.246, which represents low consistency. Consequently, PCR based BLV technique was considered as a corrective measure for diagnosis of BLV infection in Holstein dairy cattle.

• International Journal of Fluid Machinery and Systems
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• v.2 no.1
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• pp.61-71
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• 2009

Machine fault diagnostic and prognostic techniques have been the considerable subjects of condition-based maintenance system in the recent time due to the potential advantages that could be gained from reducing downtime, decreasing maintenance costs, and increasing machine availability. For the past few years, research on machine fault diagnosis and prognosis has been developing rapidly. These publications covered in the wide range of statistical approaches to model-based approaches. With the aim of synthesizing and providing the information of these researches for researcher's community, this paper attempts to summarize and classify the recent published techniques in diagnosis and prognosis of rotating machinery. Furthermore, it also discusses the opportunities as well as the challenges for conducting advance research in the field of machine prognosis.

• Industrial Engineering and Management Systems
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• v.8 no.4
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• pp.247-256
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• 2009

In mass production industries such as steel making that have large equipment, sudden stops of production process due to machine failure can cause severe problems. To prevent such situations, machine diagnosis techniques play important roles. Many methods have been developed focusing on this subject. In this paper, we propose a method for the early detection of the failure on rotating machine, which is the most common theme in the machine failure detection field. A simplified method of calculating autocorrelation function is introduced and is utilized for ARMA model identification. Furthermore, an absolute deterioration factor such as Bicoherence is introduced. Machine diagnosis can be executed by this simplified calculation method of system parameter distance with weight. Proposed method proved to be a practical index for machine diagnosis by numerical examples.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.132-134
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• 2006

To improve equipment throughput and device yield, a malfunction in plasma equipment should be accurately diagnosed. A recognition model for plasma diagnosis was constructed by applying neural network to scanning electron microscope (SEM) image of plasma-etched patterns. The experimental data were collected from a plasma etching of tungsten thin films. Faults in plasma were generated by simulating a variation in process parameters. Feature vectors were obtained by applying direct and wavelet techniques to SEM Images. The wavelet techniques generated three feature vectors composed of detailed components. The diagnosis models constructed were evaluated in terms of the recognition accuracy. The direct technique yielded much smaller recognition accuracy with respect to the wavelet technique. The improvement was about 82%. This demonstrates that the direct method is more effective in constructing a neural network model of SEM profile information.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.822-827
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• 2004

In this paper, an intelligent fault diagnosis system is proposed for induction motors through the combination of feature extraction, genetic algorithm (GA) and neural network (ANN) techniques. Features are extracted from motor vibration signals, while reducing data transfers and making on-line application available. GA is used to select most significant features from whole feature database and optimize the ANN structure parameter. Optimized ANN diagnoses the condition of induction motors online after trained by the selected features. The combination of advanced techniques reduces the learning time and increases the diagnosis accuracy. The efficiency of the proposed system is demonstrated through motor faults of electrical and mechanical origin on the induction motors. The results of the test indicate that the proposed system is promising for real time application.

• Proceedings of the KIEE Conference
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• 2005.05b
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• pp.4-6
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• 2005

This paper deals with the technical map of diagnosis for power cable systems. For this purpose, failure status, failure and degradation causes, and newly suggested diagnostic techniques were investigated and analyzed. From the analysis, the technical map of diagnosis for power cable systems was proposed.

• Journal of Korea Multimedia Society
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• v.20 no.11
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• pp.1811-1819
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• 2017

As the induction motor is the core production equipment of the industry, it is necessary to construct a fault prediction and diagnosis system through continuous monitoring. Many researches have been conducted on motor fault diagnosis algorithm based on signal processing techniques using Fourier transform, neural networks, and fuzzy inference techniques. In this paper, we propose a fault diagnosis method of induction motor using LPC and DNN. To evaluate the performance of the proposed method, the fault diagnosis was carried out using the vibration data of the induction motor in steady state and simulated various fault conditions. Experimental results show that the learning time of our proposed method and the conventional spectrum+DNN method is 139 seconds and 974 seconds each executed on the experimental PC, and our method reduces execution time by 1/8 compared with conventional method. And the success rate of the proposed method is 98.08%, which is similar to 99.54% of the conventional method.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.32A no.9
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• pp.114-120
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• 1995

It is important to test the various kinds of interconnect faults between chips on a card/module. When boundary scan design techniques are adopted, the chip to chip interconnection test generation and application of test patterns is greatly simplified. Various test generation algorithms have been developed for interconnect faults. A new interconnect test generation algorithm is introduced. It reduces the number of test patterns by half over present techniques. It also guarantees the complete diagnosis of mutiple interconnect faults.