• 융합보안논문지
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• 제9권2호
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• pp.1-6
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• 2009

대부분의 진단 알고리즘은 PMC 모델을 바탕으로 결함의 개수가 t개를 초과하지 않는다는 t-진단가능 시스템의 특성을 이용한다. 그러나 병렬처리 시스템의 규모가 커짐에 따라 시스템 안에 존재하는 결함의 빈도수가 높아지게 된다. 진단 알고리즘에서 가정하는 결함의 개수 t는 시스텝 안에 있는 노드의 수에 비해 상당히 작은 개수이며, 결함의 개수가 t개를 초과하는 경우에 대하여 진단에 대한 연구가 거의 이루어지지 않았다. 이 논문에서는 결함의 개수가 t개를 초과하는 경우에 대하여 진단의 정확여부를 판단할 수 없는 충분히 작은 개수의 노드가 존재한다는 것을 허락함으로서, 진단 가능한 결함의 최대 수를 증가시키는 알고리즘을 제안한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.125.4-125
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• 2001

The problem of recongurable ight control is investigated, focusing on model reference adaptive control(MRAC) through imprecise fault diagnosis. The method integrates the fault detection and isolation(FDI) scheme with the model reference adaptive control, and can be implemented on-line and in real-time. The algorithm can cope with the fast varying parameters. The Simulation results demonstrate the ability of reconguration to maintain the stability and acceptable performance after a failure.

• Advances in aircraft and spacecraft science
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• 제7권1호
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• pp.1-17
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• 2020

In this paper, a new model-based Fault Detection and Diagnosis (FDD) method for an agile supersonic flight vehicle is presented. A nonlinear model, controlled by a classical closed loop controller and proportional navigation guidance in interception scenario, describes the behavior of the vehicle. The proposed FDD method employs the Inertial Navigation System (INS) data and nonlinear dynamic model of the vehicle to inform fins damage to the controller before leading to an undesired performance or mission failure. Broken, burnt, unactuated or not opened control surfaces cause a drastic change in aerodynamic coefficients and consequently in the dynamic model. Therefore, in addition to the changes in the control forces and moments, system dynamics will change too, leading to the failure detection process being encountered with difficulty. To this purpose, an equivalent aerodynamic model is proposed to express the dynamics of the vehicle, and the health of each fin is monitored by the value of a parameter which is estimated using an adaptive robust filter. The proposed method detects and isolates fins damages in a few seconds with good accuracy.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권1호
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• pp.33-37
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• 2002

Insulation aging diagnosis system provides early warning regarding electrical equipment defects. Early warning is very important in that it can avoid great losses resulting from unexpected shutdown of the production line. Since relations of insulation aging and partial discharge dynamics are non-linear. it is very difficult to provide early warning in an electrical equipment. In this paper, we propose the design method of insulation aging diagnosis system that use a electromagnetic wave and acoustic signal to diagnose an electrical equipment. Proposed system measures the partial discharge on-line from DAS(Data Acquisition System and acquires 2D patterns from analyzing it. For filtering the noise contained in sensor signals we used ICA algorithms. Using this data, we design of the neuro-fuzzy model that diagnoses an electrical equipment and is investigated in this paper. Validity of the new method is asserted by numerical simulation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.132-134
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• 2006

To improve equipment throughput and device yield, a malfunction in plasma equipment should be accurately diagnosed. A recognition model for plasma diagnosis was constructed by applying neural network to scanning electron microscope (SEM) image of plasma-etched patterns. The experimental data were collected from a plasma etching of tungsten thin films. Faults in plasma were generated by simulating a variation in process parameters. Feature vectors were obtained by applying direct and wavelet techniques to SEM Images. The wavelet techniques generated three feature vectors composed of detailed components. The diagnosis models constructed were evaluated in terms of the recognition accuracy. The direct technique yielded much smaller recognition accuracy with respect to the wavelet technique. The improvement was about 82%. This demonstrates that the direct method is more effective in constructing a neural network model of SEM profile information.

• Journal of Mechanical Science and Technology
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• 제18권12호
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• pp.2174-2181
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• 2004

The metal processing system usually consists of various components such as motors, work rolls, backup rolls, idle rolls, sensors, etc. Even a simple fault in a single component in the system may cause a serious damage on the final product. It is, therefore, necessary to diagnose the faults of the components to detect and prevent a system failure. Especially, the defects in a work roll are critical to the quality of strip. In this study, a new 3-D diagnosis method was developed for roll shape defects in rolling processes. The new method was induced from analyzing the rolling mechanism by using a rolling force model, a tension model, the Hitchcock's equation, and measurement of the strip thickness, etc. Computer simulation shows that the proposed method is very useful in the diagnosis of the 3-D roll shape.

• 조명전기설비학회논문지
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• 제26권3호
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• pp.61-68
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• 2012

This paper deals with a fault diagnosis algorithm for open faults in the switching devices of PWM inverter-fed IPMSM (Interior Permanent Magnet Synchronous Motor) drive. The proposed diagnostic algorithm is realized in the controller using the informations of three-phase currents or reference line-to-line voltages, without requiring additional equipments for fault detection. Under switch open fault conditions, the conventional dq model used to control an AC motor cannot directly be applied for the analysis of drive system, since three-phase balanced condition does not hold. To overcome this limitation, a fault model based on the line-to-line voltages is employed for the simulation studies. For comparative performance evaluation through the experiments, the entire control system is implemented using digital signal processor (DSP) TMS320F28335. Simulations and experimental results are presented to verify the validity of the proposed diagnosis algorithm.

• Nuclear Engineering and Technology
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• 제36권1호
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• pp.73-82
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• 2004

The deaerator of a power plant is one of feedwater heaters in the secondary system, and it is located above the feedwater pumps. The feedwater pumps take the water from the deaerator storage tank, and the net positive suction head(NSPH) should always be ensured. To secure the sufficient NPSH, the deaerator tank is equipped with the level control system of which level sensors are critical items. And it is necessary to ascertain the sensor state on-line. For this, a model-based fault detection and diagnosis(FDD) is introduced in this study. The dynamic control model is formulated from the relation of input-output flow rates and liquid-level of the deaerator storage tank. Then an adaptive state estimator is designed for the fault detection and diagnosis of sensors. The performance and effectiveness of the proposed FDD scheme are evaluated by applying the operation data of Yonggwang Units 3 & 4.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 춘계학술대회 논문집 전기설비전문위원
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• pp.171-173
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• 2008

This paper presents stochastic methodology based fault diction and diagnosis algorithm for induction motor systems. First, we construct probability distribution model from healthy motors and then probability distribution for faulty motors is recursively calculated by means of the proposed probability estimation. We measure motor current with hall sensors as system state. The estimated probability is compared to the model to generate a residue signal which is utilized for fault detection and diagnosis, that is, where a fault is occurred. We carry out real-time induction motor experiment to evaluate efficiency and reliability of the proposed approach.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.714-717
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• 1996

This paper presents a fault detection and diagnosis methodologies based on weighted symptom model and pattern matching between the coming fault propagation trend and the simulated one. At the first step, backward chaining is used to find the possible cause candidates for the faults. The weighted symptom model(WSM) is used to generate those candidates. The weight is determined from dynamic simulation. Using WSMs, the methodology can generate the cause candidates and rank them according to the probability. Secondly, the fault propagation trends identified from the partial or complete sequence of measurements are compared to the standard fault propagation trends stored a priori. A pattern matching algorithm based on a number of triangular episodes is used to effectively match those trends. The standard trends have been generated using dynamic simulation and stored a priori. The proposed methodology has been illustrated using two case studies and showed satisfactory diagnostic resolution.