• Journal of Power Electronics
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• v.16 no.3
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• pp.1012-1024
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• 2016

This study deals with an on-line software fault detection, localization, and system reconfiguration method for electrical system drives composed of three-phase AC/DC/AC converters and three-phase permanent magnet synchronous machine (PMSM) drives. Current sensor failure (outage), speed/position sensor loss (disconnection), and damaged DC-link voltage sensor are considered faults. The occurrence of these faults in PMSM drive systems degrades system performance and affects the safety, maintenance, and service continuity of the electrical system drives. The proposed method is based on the monitoring signals of "abc" currents, DC-link voltage, and rotor speed/position using a measurement chain. The listed signals are analyzed and evaluated with the generated residuals and threshold values obtained from a Sliding Mode Current-Speed-DC-link Voltage Observer (SMCSVO) to acquire an on-line fault decision. The novelty of the method is the faults diagnosis algorithm that combines the use of SMCSVO and adaptive thresholds; thus, the number of false alarms is reduced, and the reliability and robustness of the fault detection system are guaranteed. Furthermore, the proposed algorithm's performance is experimentally analyzed and tested in real time using a dSPACE DS 1104 digital signal processor board.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.10
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• pp.880-887
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• 2005

This paper presents a fault diagnosis(detection and isolation) approach for the Ammunition Transport Automation system(ATAS). Due to limited time and information available during its cyclic operation, the on-line fault detection algorithm consists of sequential test logics referring to the normal states, which can be considered as a kind of expert system. If a failure were detected, the off-line isolation algorithm finds the fault location through trained ART2 neural network. By the results of simulations and some on-line field test, it has been shown that the presented approach is effective enough and applicable to related automation systems.

• Journal of IKEEE
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• v.27 no.4
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• pp.601-609
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• 2023

In this paper, we propose an artificial intelligence-based high-speed fault diagnosis method using an FPGA in the event of a ground fault in a DC system. When applying artificial intelligence algorithms to fault diagnosis, a substantial amount of computation and real-time data processing are required. By employing an FPGA with AI-based high-speed fault diagnosis, the DC breaker can operate more rapidly, thereby reducing the breaking capacity of the DC breaker. therefore, in this paper, an intelligent high-speed diagnosis algorithm was implemented by collecting fault data through fault simulation of a DC system using Matlab/Simulink. Subsequently, the proposed intelligent high-speed fault diagnosis algorithm was applied to the FPGA, and performance verification was conducted.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1705-1711
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• 1991

On-line fault detection and diagnosis has an increasing interest in a chemical process industry, especially for a process control and automation. The chemical process needs an intelligent operation-aided workstation which can do such tasks as process monitoring, fault detection, fault diagnosis and action guidance in semiautomatic mode. These tasks can increase the performance of a process operation and give merits in economics, safety and reliability. Aiming these tasks, series of researches have been done in our lab. Main results from these researches are building appropriate knowledge representation models and a diagnosis mechanism for fault detection and diagnosis in a chemical process. The knowledge representation schemes developed in our previous research, the symptom tree model and the fault-consequence digraph, showed the effectiveness and the usefulness in a real-time application, of the process diagnosis, especially in large and complex plants. However in our previous approach, the diagnosis speed is its demerit in spite of its merits of high resolution, mainly due to using two knowledge models complementarily. In our current study, new knowledge representation scheme is developed which integrates the previous two knowledge models, the symptom tree and the fault-consequence digraph, into one. This new model is constructed using a material balance, energy balance, momentum balance and equipment constraints. Controller related constraints are included in this new model, which possesses merits of the two previous models. This new integrated model will be tested and verified by the real-time application in a BTX process or a crude unit process. The reliability and flexibility will be greatly enhanced compared to the previous model in spite of the low diagnosis speed. Nexpert Object for the expert system shell and SUN4 workstation for the hardware platform are used. TCP/IP for a communication protocol and interfacing to a dynamic simulator, SPEEDUP, for a dynamic data generation are being studied.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.927-929
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• 1997

In this paper, a new expert system scheme, called Logic Based Expert System (LBES), is proposed for real time fault diagnosis of power system. In LBES, Expertise is represented by logical connectives and converted into a Boolean function. The set of Prime Implicants (PIs) of the Boolean function contains all the sound inference results which can be obtained from the expertise. Therefore, off-line inference is possible by off-line PI identification, which reduces the on-line inference time considerably and makes it possible to utilize-the LBES in real-time environment.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.11
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• pp.968-973
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• 2000

Fault diagnosis plays an important role in the performance and safe operation of many modern engineering plants. This paper investigates the problem of fault detection using neural networks in dynamic systems. A general framework for constructing a nonlinear fault detection scheme for nonlinear dynamic systems containing modeling uncertaintly is proposed. The main idea behind the proposed approach is to monitor the physical system with an off -line learning neural network and then to approximate the upper and lower thresholds of acceleration of the nominal system with the model-based threshold(ThMB) method, The performance of the proposed fault detection scheme is investigated through simulations of a pendulum with uncertainty.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.12
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• pp.1288-1296
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• 2001

A scheme for on-line fault detection and diagnosis of an air-handling unit is presented. The fault detection scheme uses residuals which are generated by comparing each measurement with analytical redundancies computed from the reference models. In this paper, artificial neural networks (ANNs) are used to estimate analytical redundancy and to classify faults. The Lebenburg-Marquardt algorithm is used to train feed forward ANNs that provide estimates of continuous states and diagnosis results. The simulation result demonstrated that the ANNs can effectively detect and diagnose faults in the highly non-linear and complex HVAC systems.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.18 no.2
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• pp.153-158
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• 2005

Recently, as the feasibility study shows that trans-Korea railway and trans-continental railway are advantageous, interest in high-speed railway system is increasing. Because railway vehicle is environment-friendly and safe compared with airplane and ship, its market-sharing increases gradually. KHST(Korean High Speed Train) has been developed by KRRI (Korea Railroad Research Institute) for last 6 years to satisfy the need. An electric railway system is composed of high-tech subsystems, among which main electric equipment such as transformers and converter are critical components determining the performance of rolling stock. We developed a measurement system for on-line test and evaluation of performances of KHST. The measurement system is composed of software part and hardware part. Perfect interface between multi-users is possible. A now method to measure temperature was applied to the measurement system. By using the system, fault diagnosis and performance evaluation of electric equipment in Korean High Speed Train was conducted during test running.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.60 no.3
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• pp.114-119
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• 2011

Large Power transformer is a complex and critical component of power plant and consists of cellulosic paper, insulation oil, core, coil etc. Insulation materials of transformer and related equipment break down to liberate dissolved gas due to corona, partial discharge, pyrolysis or thermal decomposition. The dissolved gas kinds can be related to the type of electrical faults, and the rate of gas generation can indicate the severity of the fault. The identities of gases being generated are using very useful to decide the condition of transformation status. Therefore dissolved gas analysis is one of the best condition monitoring methods for power transformer. Also, on-line multi-gas analyzer has been developed and installed to monitor the condition of critical transformers. Rogers method, IEC method, key gas method and Duval Triangle method are used to failure diagnosis typically, and those methods are using the ratio or kinds of dissolved gas to evaluate the condition of transformer. This paper analyzes the reliability of transformer diagnostic methods considering actual dissolved gas concentration. Fault diagnosis is performed based on the dissolved gas of five transformers which experienced various fault respectively in the field, and the diagnosis result is compared with the actual off-line fault analysis. In this comparison result, Diagnostic methods using dissolved gas ratio like Rogers method, IEC method are sometimes fall outside the ratio code and no diagnosis but Duval triangle method and Key gas method is correct comparatively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3779-3784
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• 2012

Real-time process monitoring and diagnosis of industrial processes is one of important operational tasks for quality and safety reasons. The objective of fault diagnosis or identification is to find process variables responsible for causing a specific fault in the process. This helps process operators to investigate root causes more effectively. This work assesses the applicability of combining a nonlinear statistical technique of kernel Fisher discriminant analysis with a preprocessing method as a tool of on-line fault identification. To compare its performance to existing linear principal component analysis (PCA) identification scheme, a case study on a benchmark process was performed to show that the fault identification scheme produced more reliable diagnosis results than linear method.