• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.115-131
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• 1998

Customer service process is one of the most important processes in today's competitive business environment. Among the various activities of customer service process, equipment malfunction diagnosis activity should be performed fast and accurately. When a customer calls the service center and reports the observed symptoms, he/she describes them in layman's terms. Therefore, the customer-reported symptoms have not been considered helpful information for service representatives. However, in order to perform diagnosis activity fast and accurately, we need to make use of the customer-reported symptoms actively. In this research, we developed three systems called R-EMD (Rule-based Equipment Malfunction Diagnostic system), C-EMD (Case-based Equipment Malfunction Diagnostic system) and R&C-EMD (Rule & Case-based Equipment Malfunction Diagnostic system), each of which diagnoses equipment malfunctions using the customer-reported symptoms. R&C-EMD is a hybrid system that utilizes both rule-based and case-based technologies. The diagnosis rules used in R&C-EMD and R-EMD were not acquired from service manuals or interviews with service representatives. Rater, we extracted them directly from the past diagnosis cases based on symptoms' frequencies. By this way, we were able to overcome the knowledge acquisition bottleneck. Using the real 100 malfunction diagnosis cases, we evaluated the performances of R&C-EMC, R-EMD and C-EMD in terms of speed and accuracy. In diagnosis time, R&C-EMD took longer than R-EMD and shorter than C-EMD. However, R&C-EMC was the best in accuracy.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.92-97
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• 2001

The major faults of CNC machine tool is operational error which is charge over 70%. This paper describes model of remote service and fault diagnosis for CNC machine tool with open architecture controller. For intelligent fault diagnosis, new model is proposed. In this paper, the three major operational faults, emergency stop error, cycle start disable and machine ready disable, are defined. Two diagnostic models based on the ladder diagram, switching function model, step switching function model, are proposed. For internet based remote service, suitable environment is proposed and implemented with web server and client.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.859-866
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• 2021

Agricultural Photovoltaic power generation is a new model that installs solar power generation facilities on top of farmland. Through this, it is possible to increase farm household income by producing crops and electricity at the same time. Recently, various attempts have been made to utilize agricultural solar power generation. Agricultural photovoltaic power generation has a disadvantage in that maintenance is relatively difficult because it is installed on a relatively high structure unlike conventional photovoltaic power generation. To solve these problems, intelligent and efficient operation and diagnostic functions are required. In this paper, we discuss the design and implementation of a prediction and diagnosis system to collect and store the power output of agricultural solar power generation facilities and implement an intelligent prediction model. The proposed system predicts the amount of power generation based on the amount of solar power generation and environmental sensor data, determines whether there is an abnormality in the facility, calculates the aging degree of the facility and provides it to the user.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.2
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• pp.129-134
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• 2002

Operating the nuclear power generations safely is not easy way because nuclear power generations are very complicated systems. In the main control room of the nuclear power generations, about 4000 numbers of alarms and monitoring devices are equipped to handle the signals corresponding to operating equipments. Thus, operators have to deal with massive information and to analyze the situation immediately. If they could not achieve these task, then they should make big problem in the power generations. Owing to too many variables, operators could be also in the uncontrolled situation. So in this paper, the fault diagnosis system is designed using 2-steps neural networks. This diagnosis method is based on the pattern of the principal variables which could represent the type and severity of faults.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.57-62
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• 2015

In this paper, we design fuzzy inference-based deterioration diagnosis system through different image for rapid as well as efficient diagnosis of electrical equipments. When the deterioration diagnosis of the electrical equipment starts, abnormal state of assigned area is detected by comparing with the temperature of the first normal state of the area. Deterioration state of detected area is diagnosed by using fuzzy inference algorithm. In the fuzzy inference algorithm, fuzzy rules are defined by If-then form and are described as look-up table. Both temperature and its ensuing variation are used as input variables. While triangular membership function is used for the fuzzy input variables of fuzzy rules, singleton membership function is used for the output variable of fuzzy rules. The final output is calculated by using the center of gravity of fuzzy inference method. Experimental data acquired from individual electrical equipments is used in order to evaluate the output performance of the proposed system.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.673-681
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• 2005

As it is the most important to make sure security and reliability for nuclear Power Plant, it's considered the most crucial issues to develop a fault detective and diagnostic system in spite of multiple hardware redundancy in itself. To develop an algorithm for a fault diagnosis in the nuclear PWR steam generator, this paper proposes a method based on ART2(adaptive resonance theory 2) neural network that senses and classifies troubles occurred in the system. The fault diagnosis system consists of fault detective part to sense occurred troubles, parameter estimation part to identify changed system parameters and fault classification part to understand types of troubles occurred. The fault classification part Is composed of a fault classifier that uses ART2 neural network. The Performance of the proposed fault diagnosis a18orithm was corroborated by applying in the steam generator.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.383-388
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• 2006

SBR is one of the most general sewage/wastewater treatment processes and, particularly, has an advantage in high concentration wastewater treatment like sewage wastewater. A Kernel PCA based fault diagnosis system for biological reaction in full-scale wastewater treatment plant was proposed using only common bio-chemical sensors such as ORP(Oxidation-Reduction Potential) and DO(Dissolved Oxygen). During the SBR operation, the operation status could be divided into normal status and abnormal status such as controller malfunction, influent disturbance and instrumental trouble. For the classification and diagnosis of these statuses, a series of preprocessing, dimension reduction using PCA, LDA, K-PCA and feature reduction was performed. Also, the diagnosis result using differential data was superior to that of raw data, and the fusion data show better results than other data. Also, the results of combination of K-PCA and LDA were better than those of LDA or (PCA+LDA). Finally, the fault recognition rate in case of using only ORP or DO was around maximum 97.03% and the fusion method showed better result of maximum 98.02%.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.341-346
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• 2008

This paper is to develop a monitoring system with diagnosis for smart cargo sensors that is for management and maintenance of the liquid cargo ships. The main goal of the system is to achieve the total automation system of the cargo sensor. By this study, the active smart sensor for the liquid cargo ships is designed and developed that guarantees high-confidence, stability, and durability. The proposed system consists of a monitoring part of the steam pressure, high-level monitoring, over flowing monitoring, gas monitoring, and tank temperature monitoring. The signals transferred from each unit system are used for sensor diagnosis based on confidence and accuracy. Finally, in this study, the total supervisory monitoring system is developed to maintain and manage the cargo effectively based on fault diagnosis and prognosis of the each sensor system.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.691-697
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• 2006

Induction motors are critical components in industrial process. So there are many research in the condition based maintenance, online monitoring system, and fault detection. This paper presents a scheme on the detection and diagnosis of the three-phase squirrel induction motor under unbalanced voltage, broken rotor bar, and a combination of these two faults. Actually one fault happen in operation, it influence other component in motor or cause another faults. Accordingly it is useful to diagnose and detect a combination fault in induction motor as well as each fault. The proposed fault detection and diagnosis algorithm is based on the stator currents from the squirrel induction motor and simulated with the aid of Matlab Simulink.

• Nuclear Engineering and Technology
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• v.50 no.8
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• pp.1306-1313
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• 2018

The randomness and incipient nature of certain faults in reactor systems warrant a robust and dynamic detection mechanism. Existing models and methods for fault diagnosis using different mathematical/statistical inferences lack incipient and novel faults detection capability. To this end, we propose a fault diagnosis method that utilizes the flexibility of data-driven Support Vector Machine (SVM) for component-level fault diagnosis. The technique integrates separately-built, separately-trained, specialized SVM modules capable of component-level fault diagnosis into a coherent intelligent system, with each SVM module monitoring sub-units of the reactor coolant system. To evaluate the model, marginal faults selected from the failure mode and effect analysis (FMEA) are simulated in the steam generator and pressure boundary of the Chinese CNP300 PWR (Qinshan I NPP) reactor coolant system, using a best-estimate thermal-hydraulic code, RELAP5/SCDAP Mod4.0. Multiclass SVM model is trained with component level parameters that represent the steady state and selected faults in the components. For optimization purposes, we considered and compared the performances of different multiclass models in MATLAB, using different coding matrices, as well as different kernel functions on the representative data derived from the simulation of Qinshan I NPP. An optimum predictive model - the Error Correcting Output Code (ECOC) with TenaryComplete coding matrix - was obtained from experiments, and utilized to diagnose the incipient faults. Some of the important diagnostic results and heuristic model evaluation methods are presented in this paper.