• Journal of Electrical Engineering and Technology
• /
• v.2 no.3
• /
• pp.353-357
• /
• 2007

The bearing diagnostics method is presented in this paper using fuzzy inference based on vibration data. Both time-domain and frequency-domain features are used as input data for bearing fault detection. The Adaptive Network based Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA) have been proposed to select the fuzzy model input and output parameters. Training results give the optimized fuzzy inference system for bearing diagnosis based on measured vibration data. The result is also tested with other sets of bearing data to illustrate the reliability of the chosen model.

• Proceedings of the KIPE Conference
• /
• 2004.07a
• /
• pp.224-227
• /
• 2004

The goal of this paper is optimal switching angle of switched reluctance motor drive system for maximum energy ratio. A new magnetizing method with a low-frequency increasing the energy conversion ratio that is related to the efficiency of motor is proposed. As results, it improved the efficiency about $2[\%]$. And a torque ripple is also sufficiently reduced compared with that of the conventional switching angle magnetizing approach. In order to start softly regardless of a large ripple torque, the profile of phase current is predicted by the ANFIS, and current control mode was adapted when it is operated under the starting speed. Variable implementations on the fields will guarantee the more practical drive system.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.8
• /
• pp.695-700
• /
• 2001

We design the intelligent diagnosis system for deciding on operation of TMS Analysis in this paper. We propose the method to model the neuro-fuzzy model for diagnosing the operation state of analyzer by using input and output signals of TMS and Expert's experiment data. Validity of the proposed system is asserted by numerical simulation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2001.10a
• /
• pp.386-392
• /
• 2001

We design the intelligent diagnosis system for deciding on operation state of TMS Analyzer in this paper. We propose the method to model the neuro-fuzzy model for diagnosing theoperation state of analyzer by using input and output signals of TMS to measure Nox and SOx. By using experiment data, neuro-fuzzy model is investigated. Validity of the proposed system is asserted by numerical simulation.

• Proceedings of the KIEE Conference
• /
• 1998.07c
• /
• pp.1046-1049
• /
• 1998

This paper deals with the new fault detection technique for transmission line using Neuro-fuzzy Scheme. Neuro-fuzzy Scheme is ANFIS(Adaptive-network Fuzzy Inference System) based on fusion of fuzzy logic and neural networks. The proposed scheme has five layers. Each layer is the component of fuzzy Inference system and performs different action. Using learning method of neural network, fuzzy premise and consequent parameters is tuned properly.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.4 no.2
• /
• pp.187-192
• /
• 2004

In this paper, we proposed the method to design fuzzy controller using the experience of the operating expert and experimental numeric data for the robust control about the noise and disturbance instead of the traditional PID controller for the main steam temperature control of the thermal power plant. The temperature of main steam temperature process has to be controlled uniformly for the stable electric power output. The process has the problem of the hunting for the cases of various disturbances. In that case, the manual action of the operator happened to be introduced in some cases. We adopted the TSK (Takagi-Sugeno-Kang) model as the fuzzy controller and designed the fuzzy rules using the informations extracted directly from the real plant and various operating condition to solve the above problems and to apply practically. We implemented the real fuzzy controller as the Function Block module in the DCS(Distributed Control System) and evaluated the feasibility through the experimental results of the simulation.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.132-132
• /
• 2021

Access to accurate spatial precipitation in many hydrological studies is necessary. Existence of many mountains with diverse topography in South Korea causes different spatial distribution of precipitation. Rain gauge stations show accurate precipitation information in points, but due to the limited use of rain gauge stations and the difficulty of accessing them, there is not enough accurate information in the whole area. Weather radars can provide an integrated precipitation information spatially. Despite this, weather radar data have some errors that can not provide accurate data, especially in heavy rainfall. In this study, some location-based variable like aspect, elevation, plan curvature, profile curvature, slope and distance from the sea which has most effect on rainfall was considered. Then Automatic Weather Station data was used for spatial training of variables in each event. According to this, K-fold cross-validation method was combined with Adaptive Neuro-Fuzzy Inference System. Based on this, 80% of Automatic Weather Station data was used for training and validation of model and 20% was used for testing and evaluation of model. Finally, spatial distribution of precipitation for 1×1 km resolution in Gwangdeoksan radar station was estimates. The results showed a significant decrease in RMSE and an increase in correlation with the observed amount of precipitation.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.6
• /
• pp.571-576
• /
• 2002

There have been a lot of considerations dealing with improving the performance of neuro-fuzzy system. The studies on the neuro-fuzzy modeling have largely been devoted to two approaches. First is to improve performance index of system. The other is to reduce the structure size. In spite of its satisfactory result, it should be noted that these are difficult to extend to high dimensional input or to increase the membership functions. We propose a novel neuro-fuzzy system based on the efficient clustering method for initializing the parameters of the premise part. It is a very useful method that maintains a few number of rules and improves the performance. It combine the various algorithms to improve the performance. The Expectation-Maximization algorithm of Gaussian mixture model is an efficient estimation method for unknown parameter estimation of mirture model. The obtained parameters are used for fuzzy clustering method. The proposed method satisfies these two requirements using the Gaussian mixture model and neuro-fuzzy modeling. Experimental results indicate that the proposed method is capable of giving reliable performance.

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

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1539-1544
• /
• 2005

The gauge control of the fishing mill is very important because more and more accurately sized hot rolled coils are demanded by customers recently. Because the mill constant and the plasticity coefficient vary with the specifications of the mill, the classification of steel, the strip width, the strip thickness and the slab temperature, the variation of these parameters should be considered in the automatic gauge control system(AGC). Generally, the AGC gain is used to minimize the effect of the uncertain parameters. In a practical field, operators set the AGC gain as a constant value calculated by FSU (Finishing-mill Set-Up model) and it is not changed during the operating time. In this paper, the thickness data signals that occupy different frequency bands are respectively extracted by adaptive filters and then the main cause of the thickness variation is analyzed. Additionally, the AGC gain is adaptively tuned to reduce this variation using the online tuning model. Especially ANFIS(Adaptive-Neuro-based Fuzzy Interface System) which unifies both fuzzy logics and neural networks, is used for this gain adjustment system because fuzzy logics use the professionals' experiences about the uncertainty and the nonlinearity of the system. Simulation is performed by using POSCO's data and the results show that proposed on-line gain adjustment algorithm has a good performance.