• 한국신뢰성학회지:신뢰성응용연구
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• 제14권1호
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• pp.37-43
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• 2014

This paper deals with a design of observer based fault detection filter for a boiler-turbine control system. The goal is to present a method for rapid sensor fault detection in order to enhance the reliability of boiler-turbine operation in the thermal power plant. Our fault detection filter can be designed via solutions of linear matrix inequalities. In order to demonstrate the efficacy of our design method, numerical simulations are provided.

• 설비공학논문집
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• 제16권10호
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• pp.932-941
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• 2004

The objective of this study is to detect the multi-fault of HVAC system using a new pattern classification technique. To classify the effect of single-fault in determining the pattern, supply air temperature, OA-damper, supply fan, and air flowrate were chosen as experimental parameters. The combination of supply temperature, flow rate, supply fan and OA-damper were chosen as multi-fault conditions. Three kinds of patterns were introduced in the analysis of multi-fault problem. To solve multi-fault problem, the new pattern classification technique using residual ratio analysis was introduced to detect the multi-fault as well as single-fault. The residual ratio could diagnose single-fault or multi-fault into several patterns.

• 대한기계학회논문집B
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• 제29권11호
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• pp.1220-1228
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• 2005

Fault detection and diagnostic (FDD) systems have the potential to reduce equipment downtime, service costs, and utility costs. In this study, model based algorithm and fuzzy algorithm were used to detect and diagnose various fault at System multi-air conditioner. various fault include the Refrigerant Low charging, Fouling of Indoor Heat Exchanger, Fouling of Outdoor Heat Exchanger A experimental verification was conducted in the 6HP System multi-air conditioner on an 8-floor building. Test results showed diagnosis result about 78 $\~$ 90$\%$ for given faults. This Study lays the foundation fur future work on develope the real-time fault detection and diagnosis system for the System multi-air conditioner.

• 대한전기학회논문지:전력기술부문A
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• 제48권2호
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• pp.136-144
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• 1999

This paper deals with the fault detection problem in uncertain linear multivariable systems and its application. A robust fault detection method presented by Kim et a. (1998) for MIMO (Multi Input/Multi Output) systems has been adopted and applied to the twin rotor MIMO experimental setup using industrial DSP. The system identification problem is formulated for the twin rotor MIMO system and its parameters are estimated using experimental data. Based on the estimated parameters, some fault detection simulations are performed using the robust fault detection method, which shows that the preformance is satisfied.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.87-87
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• 2000

In this paper, an integrated design of fault detection, diagnosis and reconfigurable control tot multi-input and multi-output system is proposed. It is based on the interacting multiple model estimation algorithm, which is one of the most cost-effective adaptive estimation techniques for systems involving structural and/or parametric changes. This research focuses on the method to recover the performance of a system with failed actuators by switching plant models and controllers appropriately. The proposed scheme is applied to a fault tolerant control design for flight control system.

• 한국정보과학회논문지:소프트웨어및응용
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• 제37권7호
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• pp.568-572
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• 2010

Mission-critical 시스템의 경우 자가 치유는 신뢰성을 보장하기 위한 기술 중 하나이다. 자가치유는 오류 탐지와 오류 회복으로 이루어져 있으며 오류 탐지는 오류 회복을 가능하게 하는 자가 치유의 중요한 첫 단계이지만 시스템에 과부하를 주는 문제가 있다. 모델 기반의 방법 등으로 오류를 탐지할 수 있는데 시스템의 모든 행위를 통지하고 정상 행위 모델과 통지된 시스템의 행위를 비교하여야 하므로 그양이 많고 부하가 크기 때문이다. 본 논문에서는 모델 기반의 오류 탐지 방법을 보완하는 아키텍처 기반의 다계층적 자가적응형 모니터링 방법을 제안한다. 소프트웨어 아키텍처 상에서 오류 탐지의 중요도는 컴포넌트 마다 다르다. 각 컴포넌트마다 발생하는 오류의 심각도와 빈도가 다르기 때문이다. 모니터링 중요도가 높은 컴포넌트에는 강도가 높고 모니터링 중요도가 낮은 컴포넌트에는 강도가 낮도록 모니터가 적응한다면 오류 탐지의 부하는 줄이고 효율은 유지시킬 수 있다. 또한 소프트웨어의 환경 변화 및 아키텍처상의 변화 등에 따라 오류 발생 빈도가 변화하여 컴포넌트의 오류 탐지 중요도가 변화하기 때문에 학습을 통해 이를 추적하여 자가적응적으로 중요도가 높은 컴포넌트를 집중 모니터링 한다.

• 전기학회논문지
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• 제66권10호
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• pp.1445-1453
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• 2017

In this paper, we propose a fault detection model based on multi-layer neural network using data mining technique for faults due to boiler tube leakage in a thermal power plant. Major measurement data related to faults are analyzed using statistical methods. Based on the analysis results, the number of input data of the proposed fault detection model is simplified. Then, each input data is clustering with normal data and fault data by applying K-Means algorithm, which is one of the data mining techniques. fault data were trained by the neural network and tested fault detection for boiler tube leakage fault.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 하계종합학술대회 논문집(5)
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• pp.115-118
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• 2001

In this paper we propose an FDI(fault detection and isolation) algorithm using neural network-based multi-fault models to detect and isolate single faults in nonlinear systems. When a change in the system occurs, the errors between the system output and the neural network nominal system output cross a threshold, and once a fault in the system is detected, the fault classifier statistically isolates the fault by using the error between each neural network-based fault model output and the system output.

• 제어로봇시스템학회논문지
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• 제9권2호
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• pp.107-116
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• 2003

This paper presents a systematic design methodology for fault tolerant controller against a fault in actuators and sensors of linear stochastic systems with uncertainties. The scheme is based on fault detection and diagnosis(isolation and estimation) using a bank of robust two-stage Kalman filters, and accommodation of the actuator fault by eigenstructure assignment and immediate compensation of the sensor's faulty measurement. In order to clarify the fault feature in test statistics of residual, noise reduction method is given by multi-scale discrete wavelet transform. The effectiveness of our approach Is shown via simulations for a VTOL(vertical take-off and landing) aircraft subjected to parameter variations, external disturbances, process and sensor noises.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• 제5B권2호
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• pp.103-110
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• 2005

Broken rotor bars in induction motors can be detected by monitoring any abnormality of the spectrum amplitudes at certain frequencies in the motor current spectrum. Broken rotor bar fault detection schemes should rely on multiple signatures in order to overcome or reduce the effect of any misinterpretation of the signatures that are obscured by factors such as measurement noises and different load conditions. Multiple Discriminant Analysis (MDA) and Artificial Neural Networks (ANN) provide appropriate environments to develop such fault detection schemes because of their multi-input processing capabilities. This paper describes two fault detection schemes for broken rotor bar fault detection with multiple signature processing, and demonstrates that multiple signature processing is more efficient than single signature processing.