• 제어로봇시스템학회논문지
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• 제18권7호
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• pp.669-672
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• 2012

This paper investigates an algorithm for robust fault diagnosis in robot manipulators. The TOSM (Third Order Sliding Mode observer) provides both theoretically exact observation and unknown fault identification without filtration. The EOI (Equivalent Output Injections) of the TOSM observers can be used as residuals for the problem of fault diagnosis and to identify the unknown faults. The obtained fault information can be used for fault detection, isolation as well as fault accommodation to the self-correcting failure system. The computer simulation results for a PUMA 560 robot are shown to verify the effectiveness of the proposed strategy.

• Journal of Power Electronics
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• 제12권1호
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• pp.120-127
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• 2012

Fault diagnosis technique of electrical drives is becoming more and more important, since voltage fed converter system has become industrial standard in many applications. Many studies have been conducted an inverter fault diagnosis for induction motors. However, there are few researches about fault diagnosis of 3-phase ac/dc PWM (Pulse Width Modulation) converter compared to the dc/ ac inverter. The ac/dc converter is the opposite of dc/ac inverter at current flow. Also, inverter and converter have different current patterns under the same condition of IGBT (Insulated gate bipolar transistor) open switch fault. Therefore, it is difficult to apply intact diagnosis methods of inverter to the converter. This paper proposes modified fault detection methods for IGBT open switch fault in 3-phase ac/dc PWM converter by modifying established fault diagnostic methods for dc/ac inverters.

• Journal of Power Electronics
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• 제10권6호
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• pp.686-693
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• 2010

In this paper, a fault diagnosis method based on fuzzy logic for the three-parallel power converter in a wind turbine system is presented. The method can not only detect both open and short faults but can also identify faulty switching devices without additional voltage sensors or an analysis modeling of the system. The location of a faulty switch can be indicated by six-patterns of a stator current vector and the fault switching device detection is achieved by analyzing the current vector. A fault tolerant algorithm is also presented to maintain proper performance under faulty conditions. The reliability of the proposed fault detection technique has been proven by simulations and experiments with a 10kW simulator.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1994년도 추계학술대회 논문집
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• pp.790-795
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• 1994

This paper describes multiple fault diagnosis method in complex system with hierarchical structure. Complex system is divided into subsystem, item, component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. We introducd to Hierarchical Artificial Neural Network(HANN) for this purpose. HANN consists of four level neural network, first level for symptom classification, second level for item fault diagnosis, third level for component symptom classification,forth level for component fault diagnosis. Each network is multi layer perceptron with 7 inputs, 30 hidden node and 7 outputs trainined by backpropagation. UNIX IPC(Inter Process Communication) is used for implementing HANN with multitasking and message transfer between processes in SUN workstation. We tested HANN in reactor system.

• International Journal of Air-Conditioning and Refrigeration
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• 제16권4호
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• pp.137-144
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• 2008

This study deals with on-line fault detection and diagnosis(FDD) for heat exchangers of a variable speed refrigeration system(VSRS) based on current information. The current residual which is the difference between real detected current from current sensors and estimated current from no fault model was utilized to diagnose faults of the heat exchangers. Comparing to the conventional FDD of constant refrigeration system based on temperature and pressure information, the suggested FDD method shows better robustness to the VSRS which has a feedback control loop. Moreover the suggested method can be expected more precise and faster diagnosis of faults about heat exchangers. Throughout some experiments, the validity of the method was verified.

• 한국소음진동공학회논문집
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• 제21권11호
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• pp.1020-1028
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• 2011

As rotating machines play an important role in industrial applications such as aeronautical, naval and automotive industries, many researchers have developed various condition monitoring system and fault diagnosis system by applying artificial neural network. Since using obtained signals without preprocessing as inputs of neural network can decrease performance of fault classification, it is very important to extract significant features of captured signals and to apply suitable features into diagnosis system according to the kinds of obtained signals. Therefore, this paper proposes a neural-network-based fault diagnosis system using AR coefficients as feature vectors by LPC(linear predictive coding) and EIV(errors-in variables) analysis. We extracted feature vectors from sound, vibration and current faulty signals and evaluated the suitability of feature vectors depending on the classification results and training error rates by changing AR order and adding noise. From experimental results, we conclude that classification results using feature vectors by EIV analysis indicate more than 90 % stably for less than 10 orders and noise effect comparing to LPC.

• 전기학회논문지
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• 제58권7호
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• pp.1276-1280
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• 2009

This paper presents a fault diagnosis expert system for 765kV substation. The proposed system includes the topology processor and intelligent alarm processing subsystems. This expert system estimates the fault section through the inference process using heuristic knowledge and the output of topology processor and intelligent alarm processing system. The rule-base of this expert system is composed of basic rules suggested by Korea Electric Power Corporation and heuristic rules. This expert system is developed using PROLOG language. Also, user friendly Graphic User Interface is developed using visual basic programming in the windows XP environment. The proposed expert system showed a promising performance through the several case studies.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2418-2423
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• 2005

Hard Disk Drive (HDD) test system is the equipment for the final test of HDD product by iterative read/write/seek test. This paper proposes an expert system for the fault diagnosis of HDD test systems. The purposed expert system is composed with two cascade inference, fuzzy logic and conventional binary logic. The fuzzy logic determines the possibility of the system fault using the test history data, then, the binary logic inferences the fault location of the test system. The proposed expert system is tested in SAMSUNG HDD product line, KUMI, KOREA, and shows satisfactory results.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2424-2429
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• 2005

Hard Disk Drive (HDD) test system is the equipment for the final test of HDD product by iterative read/write/seek test. This paper proposes an expert system for the fault diagnosis of HDD test systems. The purposed expert system is composed with two cascade inference, fuzzy logic and conventional binary logic. The fuzzy logic determines the possibility of the system fault using the test history data, then, the binary logic inferences the fault location of the test system. The proposed expert system is tested in SAMSUNG HDD product line, KUMI, KOREA, and shows satisfactory results.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권6호
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• pp.457-464
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• 2004

Hard Disk Drive (HDD) test system is an equipment for the final test of HDD product by iterative read/write/seek test. This paper proposes an expert system for the fault diagnosis of HDD test systems. The purposed expert system is composed with two cascade inference, fuzzy logic and conventional binary logic. The fuzzy logic determines the possibility of the system fault using the test history data, then, the binary logic inferences the fault location of the test system. The proposed expert system is tested in SAMSUNG HDD production line, KUMI, KOREA, and shows satisfactory results.