• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.677-682
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• 1997

Vibration diagnosis of steam turbine generator is essential for safe operation. For a fast few decades, several data base systems for diagnosis of steam turbine generators have been developed and proved useful. However, there still remains a problem in using data base systems such that they require an expert engineer who has a deep insight or knowledg into the system. Moreover,such data base systems can not give any information if the input is not completely fit with data base. This paper presents an effective method for vibration diagnosis of steam turbine generators using fuzzy inference. The proposed method includes also a strategy to overcome the drawback of data base system such that one cannot obtain any information when the input is insufficient or not exact. A computer program is written to realize the entire procedure for the diagnosis. Three realistic problems are dealt with to show the effectiveness of the proposed method.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.29 no.11 s.242
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• pp.1220-1228
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• 2005

Fault detection and diagnostic (FDD) systems have the potential to reduce equipment downtime, service costs, and utility costs. In this study, model based algorithm and fuzzy algorithm were used to detect and diagnose various fault at System multi-air conditioner. various fault include the Refrigerant Low charging, Fouling of Indoor Heat Exchanger, Fouling of Outdoor Heat Exchanger A experimental verification was conducted in the 6HP System multi-air conditioner on an 8-floor building. Test results showed diagnosis result about 78 $\~$ 90$\%$ for given faults. This Study lays the foundation fur future work on develope the real-time fault detection and diagnosis system for the System multi-air conditioner.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.10
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• pp.516-523
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• 2000

In this paper, intelligent methods using digital protective relay in power supervisory control system is developed in order to protect power systems by means of timely fault detection and diagnosis during operation for induction motor which has various load environments and capacities in power systems. The spectrum pattern of input currents was used to monitor to state of induction motors, and by clustering the spectrum pattern of input currents, the newly occurrence of spectrums pattern caused by faults were detected. For diagnosis of the fault detected, the fuzzy fault tree was derived, and the fuzzy relation equation representing the relation between an induction motor fault and each fault type, was solved. The solution of the fuzzy relation equation shows the possibility of each fault's occurring. The results obtained are summarized as follows: 1) The test result on the basis of KEMC1120 and IEC60255, show that the operation time error of the digital motor protective relay is improved within ${\pm}5%$. 2) Using clustering algorithm by unsupervisory learning, an on-line fault detection method, not affected by the characteristics of loads and rates, was implemented, and the degree of dependency by experts during fault detection was reduced. 3) With the fuzzy fault tree, fault diagnosis process became systematic and expandable to the whole system, and the diagnosis for sub-systems can be made as an object-oriented module.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.18 no.10
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• pp.791-799
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• 2006

The fault diagnosis technologies may be applied in order to decrease the energy consumption and the maintenance cost of an air-conditioning system. In this paper, a fault diagnosis algorithm was developed by using a neural no-fault model and a dual fuzzy logic. Five different faults, such as the compressor valve leakage, the liquid line blockage, the condenser fouling, the evaporator fouling, and the refrigerant leakage of an air-conditioning system, were considered. The fault diagnosis algorithm was tested by using a fault simulation facility. Test results showed that the algorithm developed for this study was effective to detect and diagnose various faults. Therefore, this algorithm may be practically used for the fault diagnosis of an air-conditioning system.

• Journal of information and communication convergence engineering
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• v.19 no.4
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• pp.234-240
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• 2021

As the number of pet dog-related businesses is rising rapidly, there is an increasing need for reliable pet dog health information systems for casual pet owners, especially those caring for older dogs. Our goal is to implement a mobile pre-diagnosis system that can provide a first-hand pre-diagnosis and an appropriate coping strategy when the pet owner observes abnormal symptoms. Our previous attempt, which is based on the fuzzy C-means family in inference, performs well when only relevant symptoms are provided for the query, but this assumption is not realistic. Thus, in this paper, we propose a hybrid inference structure that combines fuzzy association memory and a double-layered fuzzy C-means algorithm to infer the probable disease with robustness, even when noisy symptoms are present in the query provided by the user. In the experiment, it is verified that our proposed system is more robust when noisy (irrelevant) input symptoms are provided and the inferred results (probable diseases) are more cohesive than those generated by the single-phase fuzzy C-means inference engine.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1519-1524
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• 2021

In this paper, we propose a method that uses fuzzy decision tree based health pre-diagnosis system of oriental medicine. The proposed fuzzy decision tree based health pre-diagnosis system uses the data from the past which has been pre-trained to get the boundary values based on entropy then, when the user inputs the symptoms, the top 5 diseases that causes those symptoms are extracted. With the extracted top 5 diseases, the system provides information on those diseases with the cause and how to treat them with folk remedies. The database of the diseases and their symptoms is established with the information based on the various books that the oriental doctor recommended then reviewed by the oriental doctor for confirmation. By utilizing the data from the past to train the symptoms of the diseases, the proposed oriental medicine-based health pre-diagnosis system method could provide more accurate diagnosis results faster.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.04a
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• pp.253-257
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• 2000

the objective of this study is to design and evaluate a methodology for diagnosing the appendicitis in a fuzzy neural network that integrates the partition of input space by fuzzy entropy and the generation of fuzzy control rules and learning algorithm. In particular the diagnosis of appendicitis depends on the rule of thumb of the experts such that it associates with the region, the characteristics, the degree of the ache and the potential symptoms. In this scheme the basic idea is to realize the fuzzy rle base and the process of reasoning by neural network and to make the corresponding parameters of the fuzzy control rules be adapted by back propagation learning rule. To eliminate the number of the parameters of the rules, the output of the consequences of the control rules is expressed by the network's connection weights. As a result we obtain a method for reducing the system's complexities. Through computer simulations the effectiveness of the proposed strategy is verified for the diagnosis of appendicitis.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.74-77
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• 2001

One of the most important technology in PA(Factory Automation) is to construct the diagnostic system for manufacturing process. To improve the productibility in the factory, the state of tools such as bite, drill, endmill should be monitored continuously. In this study, fuzzy logic was used to check the wear of drill in drilling process. The input variables to construct the fuzzy rules are cutting force and the rate of cutting force's change. The experiment was done with the fixed spindle speed and feed rate in cutting condition.

• The Journal of the Acoustical Society of Korea
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• v.15 no.1E
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• pp.5-19
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• 1996

This paper proposes a loose part monitoring system (LPMS) design with a signal processing method based on fuzzy logic. Considering fuzzy characteristics of metallic impact waveform due to not only interferences from various types of noises in an operating nuclear power plant but also complex wave propagation paths within a monitored mechanical structure, the proposed LPMS design incorporates the comprehensive relation among impact signal features in the fuzzy rule bases for the purposes of alarm discrimination and impact diagnosis improvement. The impact signal features for the fuzzy rule bases include the rising time, the falling time, and the peak voltage values of the impact signal envelopes. Fuzzy inference results based on the fuzzy membership values of these impact signal features determine the confidence level data for each signal feature. The total integrated confidence level data is used for alarm discrimination and impact diagnosis purposes. Through the perpormance test of the proposed LPMS with mock-up structures and instrumentation facility, test results show that the system is effective in diagnosis of the loose part impact event(i.e., the evaluation of possible impacted area and degree of impact magnitude) as well as in suppressing false alarm generation.

• Journal of the Korea institute for structural maintenance and inspection
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• v.3 no.4
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• pp.129-137
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• 1999

Assessment of structural damage is a complex subject imbued with uncertainty and vagueness. This complexity arises from the use of subjective opinion and imprecise numerical data. Recently several active researches have been performed using new methods such as neural network approach or on-line damage detection. In this paper, Damage assessment (diagnosis) of the concrete bridges is studied by a new approach utilizing a neural fuzzy system that combined a neural network and a fuzzy logic. By applying this system to actual in-service bridges, it has been verified that the neural fuzzy method is effective for the bridge diagnosis.