• Journal of the Korean Society of Safety
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• v.19 no.1
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• pp.23-30
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• 2004

Modern production systems are very complex by request of automation, and failure modes that occur in thisautomatic system are very various and complex. The efficient fault diagnosis for these complex systems is essential for productivity loss prevention and cost saving. Traditional fault diagnostic system which perforns sequential fault diagnosis can cause catastrophic failure during diagnosis when fault propagation is very fast. This paper describes the Real-time Intelligent Multiple Fault Diagnosis System (RIMFDS). RIMFDS assesses current machine condition by using sensor signals. This system deals with multiple fault diagnosis, comprising of two main parts. One is a personal computer for remote signal generation and transmission and the other is a host system for multiple fault diagnosis. The signal generator generates various faulty signals and image information and sends them to the host. The host has various modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault diagnosis and graphic representation of the results. RIMFDS diagnoses multiple faults with fast fault propagation and complex physical phenomenon. The new system based on multiprocessing diagnoses by using Hierarchical Artificial Neural Network (HANN).

• Journal of Power Electronics
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• v.13 no.4
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• pp.552-557
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• 2013

This paper proposed an analytical model for the distributed magnetic field analysis of interior permanent magnet-type blush-less direct current motors under the stator-turn fault condition using the winding function theory. Stator-turn faults cause significant changes in electric and magnetic characteristic. Therefore, many studies on stator-turn faults have been performed by simulation of the finite element method because of its non-linear characteristic. However, this is difficult to apply to on-line fault detection systems because the processing time of the finite element method is very long. Fault-tolerant control systems require diagnostic methods that have simple processing systems and can produce accurate information. Thus analytical modeling of a stator-turn fault has been performed using the winding function theory, and the distributed magnetic characteristics have been analyzed under the fault condition. The proposed analytical model was verified using the finite element method.

• Journal of the Korean Society of Safety
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• v.20 no.3 s.71
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• pp.19-26
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• 2005

There are three reasoning method in fault diagnosis process. The shallow reasoning is based on the experiential knowledge and deep reasoning is based on physical model. Hybrid reasoning is mixing two type reasoning. This study describes about fault train embodiment of screw type air compressor that is used widely in industrial facilities by using various experimental method and shallow reasoning. We investigate macroscopic failure cause of air compressor through naked eye observation and then microscopic failure cause by various experimental method. We composed fault train with fault knowledge based on empirical data and scientific data that is acquired through several experiments. It is possible to analysis system reliability and failure rate with these fault train.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.1
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• pp.127-138
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• 1994

The application of fault tree technique to the analysis of compressor failure is considered. The techniques involve the decomposition of the system into a form of fault tree where certain basic events lead to a specified top event which signifies the total failure of the system. In this paper, fault trees are made by using fault train of screw type air compressor failure. The fault trees are used to obtain minimal cut sets from the modes of system failure and, hence the system failure rate for the top event can be calculated. The method of constructing fault trees and the subsequent estimation of reliability of the system is illustrated through compressor failure. It is proved that FTA is efficient to investigate the compressor failure modes and diagnose system.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.6
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• pp.556-561
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• 2009

The UAV(Unmanned Aerial Vehicle) that is remotely operating in various and long flight environments must have a very reliable propulsion system. Precise fault diagnosis of the turbo shaft engine for the Smart UAV that has the vertical take-off, landing and forward flight behaviors can promote reliability and availability. This work proposes a new diagnostic method that can identify the faulted components from engine measuring parameter changes using Fuzzy Logic and quantify its faults from the identified fault pattern using Neural Network Algorithms. The proposed diagnostic method can detect not only single fault but also multiple faults.

• The Transactions of the Korean Institute of Power Electronics
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• v.16 no.1
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• pp.79-87
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• 2011

The fault detection technique of the grid-connected photovoltaic system using wavelet transform has been suggested in this paper. The additional hardware and sensors are required to detect the inverter failure in the conventional method, and it has the disadvantage of high cost and re-design problem if the inverter specification has been changed. The suggested method used the inverter voltage and current waveform to detect the failure and the location by the wavelet coefficients variations. The prompt and accurate diagnostic function is possible using the normalized standard deviation method. The merit of the proposed method is the simple calculation and precise diagnostic capabilities of the fault detection. The computer simulation is performed and the experimental result verifies the validity of the proposed method.

• Proceedings of the Safety Management and Science Conference
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• 2000.05a
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• pp.207-215
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• 2000

This study deals with the application of knowledge-engineering and a methodology for the assessment & measurement of reliability, availability, maintainability, and safety of industrial systems using fault-tree representation. A fuzzy methodology for fault-tree evaluation seems to be an alternative solution to overcome the drawbacks of the conventional approach(insufficient information concerning the relative frequences of hazard events). To improve the quality of results, the membership functions must be approximated based on heuristic considerations. The purpose of this Is to describe the knowlwdge engineering approach, directed to integrate the various sources of knowledge involved in a FTA.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.131-134
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• 1990

A back-propagation neural network based system for a fault diagnosis of a chemical process is developed. Training data are acquired from FCD(Fault-Consequence Digraph) model. To improve the resolution of a diagnosis, the system is decomposed into 6 subsystems and the training data are composed of 0, 1 and intermediate values. The feasibility of this approach is tested through case studies in a real plant, a naphtha furnace, which has been used to develop a knowledge based expert system, OASYS (Operation Aiding expert SYStem).

• Journal of the Korea Safety Management & Science
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• v.2 no.2
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• pp.1-10
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• 2000

This study deals with the application of knowledge engineering and a methodology for the assessment and measurement of reliability, availability, maintainability, and safety of industrial systems using fault-tree representation. A fuzzy methodology for fault-tree evaluation seems to be an alternative solution to overcome the drawbacks of the conventional approach (insufficient information concerning the relative frequence of hazard events). To improve the quality of results, the membership functions must be approximated based on heuristic considerations. The purpose of this study is to describe the knowledge engineering approach, directed to integrate the various sources of knowledge involved in a FTA.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.592-596
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• 2005

The preventive diagnostic system prevents transformers from power failure by giving alarm and observing transformers in service. And it helps to establish the plan for optimum maintenance of transformer as well as to find location or cause of fault using accumulated data. KEPCO has adopted the preventive diagnostic system at nine 345kV substations since 1997. Techniques for component sensors of preventive diagnostic system were settled but diagnostic algorithm, diagnostic criteria and practical use of accumulated data are not yet established. This paper, to build up the base of preventive diagnostic algorithm for the power transformer, investigated the preventive diagnostic criteria for power transformer.