• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.4
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• pp.311-320
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• 2021

A study is performed for the real time fault diagnosis during operation and health estimation relating to performance deterioration in a turbojet engine used for an unmanned air vehicle. For this study the real time dynamic model is derived from the transient thermodynamic gas path analysis. For real fault conditions which are manipulated for the simulation, the detection techniques are applied such as Kalman filter and probabilistic decision-making approach based on statistical hypothesis test. Thereby the effectiveness is verified by showing good fault detection and isolation performances. For the health estimation with measurement parameters, it shows using an assumed performance degradation that the method by adaptive Kalman filter is feasible in practice for a condition based diagnosis and maintenance.

• Proceedings of the IEEK Conference
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• 2002.06e
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• pp.269-272
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• 2002

In this study, we designed the expert system for the diagnosis of stroke. The causes of stroke in central nervous systems are very diverse, so a doctor who treats the patients with stroke must have the expert knowledge for the quick and correct diagnosis and for the adequate medical management. But the primary physician who engaged in the primary care of the patient with stroke does not have the export knowledge for the stroke. So, we need to develop the expert system for assisting the diagnosis of stroke. Also the diagnosis system can be used as simulator for the medical students who study the neurology. In this study, we developed the diagnosis expert system that offer a pathological name provided by artificial neural networks. And we designed the inference engine and interfaces. The artificial neural network is a system that provide a possible diagnosis of stroke. We implemented the system using Windows2000 Server, IIS5.0 and ASP.

• Journal of Advanced Marine Engineering and Technology
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• v.31 no.2
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• pp.138-144
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• 2007

The variation of the crankshaft speed in a multi-cylinder engine is determined by the resultant gas pressure torque and the torsional deformation of the crankshaft. Under steady state operation, the crankshaft speed has a quasi-periodic variation. For the diagnosis the engine instantaneous speed versus crankshaft angle is utilized. This paper describes a simple measurement method of the engine instantaneous speed versus crankshaft angle using the teeth on the flywheel of the crankshaft. Two non-contacting magnetic pickup combinations detect the crank angle and TDC position for the data acquisition. The results from experiments on a 6 cylinder marine diesel engine demonstrate that the crankshaft speed variation are detected with good resolution. And the crankshaft speed variation is investigated according to the operation conditions. Also, it is confirmed that the engine output measured by EMS can be evaluated larger than the actual value due to TDC position error caused by instantaneous speed variation.

• International Journal of Aeronautical and Space Sciences
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• v.9 no.1
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• pp.100-110
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• 2008

It is not easy to monitor and identify all engine faults and conditions using conventional fault detection approaches like the GPA (Gas Path Analysis) method due to the nature and complexity of the faults. This study therefore focuses on a model based diagnostic method using Neural Network algorithms proposed for fault detection on a turbo shaft engine (PW 206C) selected as the power plant for a tilt rotor type unmanned aerial vehicle (Smart UAV). The model based diagnosis should be performed by a precise performance model. However component maps for the performance model were not provided by the engine manufacturer. Therefore they were generated by a new component map generation method, namely hybrid method using system identification and genetic algorithms that identifies inversely component characteristics from limited performance deck data provided by the engine manufacturer. Performance simulations at different operating conditions were performed on the PW206C turbo shaft engine using SIMULINK. In order to train the proposed BPNN (Back Propagation Neural Network), performance data sets obtained from performance analysis results using various implanted component degradations were used. The trained NN system could reasonably detect the faulted components including the fault pattern and quantity of the study engine at various operating conditions.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.6
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• pp.57-69
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• 2001

Expert system is one of AI area. It simulates the human's way of thinking to give solutions of problem in many applications. Most expert system consists of many components such as inference engine, knowledge base, and so on. Especially the performance of expert system depend on the control of efficiency of inference engine. Inference engine has to get features; first, if possible to minimize restrictions when it constructed the knowledge base. second, it has to serve various kinds of inferencing methods. In this paper we propose knowledge scheme for representing domain knowledge in ease, knowledge implementation technique for inferencing, and integrated knowledge-base engine with blackboard and inference engine. And we describe a expert system prototype that implemented in this paper using proposed methods, it perform diagnose about heavy industrial device. The fault diagnosis system prototype has been studied in this paper will be practical foundation in the research area of knowledge based system.

• International Journal of Aeronautical and Space Sciences
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• v.15 no.2
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• pp.123-137
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• 2014

The aviation gas turbine is composed of many expensive and highly precise parts and operated in high pressure and temperature gas. When breakdown or performance deterioration occurs due to the hostile environment and component degradation, it severely influences the aircraft operation. Recently to minimize this problem the third generation of predictive maintenance known as condition based maintenance has been developed. This method not only monitors the engine condition and diagnoses the engine faults but also gives proper maintenance advice. Therefore it can maximize the availability and minimize the maintenance cost. The advanced gas turbine health monitoring method is classified into model based diagnosis (such as observers, parity equations, parameter estimation and Gas Path Analysis (GPA)) and soft computing diagnosis (such as expert system, fuzzy logic, Neural Networks (NNs) and Genetic Algorithms (GA)). The overview shows an introduction, advantages, and disadvantages of each advanced engine health monitoring method. In addition, some practical gas turbine health monitoring application examples using the GPA methods and the artificial intelligent methods including fuzzy logic, NNs and GA developed by the author are presented.

• Journal of the Korean Society of Propulsion Engineers
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• v.12 no.3
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• pp.60-67
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• 2008

In this study, the genetic algorithm has been used for the real-time defect diagnosis on the operation of the aircraft gas-turbine engine. The component elements of the gas-turbine engine for consideration of the performance deterioration consist of the compressor, the gas generation turbine and the power turbine. Compared to the on-design point, the teaming data has been increased 200 times in case off-design conditions for the altitude, the flight mach number and the fuel consumption. Therefore, enormous learning time has been required for the satisfied convergence. The optimal division has been proposed for learning time decrease as well as the high accuracy. As results, the RMS errors of the defect diagnosis using the genetic algorithm have been confirmed under 5 %.

• 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.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.72-79
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• 2008

The weak point of the artificial neural network(ANN) is that it is easy to fall in local minima when it learns too much nonlinear data. Accordingly, the classification ratio must be low. To overcome this weakness, the hybrid method has been proposed. That is, the ANN learns data selectively after detecting the defect position by the support vector machine(SVM). First, the SVM has been used for determination of the defect position and then the magnitude of the defect has been measured by the ANN. In off-design condition, the operation region of the engine is wide and the nonlinearity of learning data increases. The module system, dividing the whole operating region into reasonably small-size sections, has been suggested to solve this problem. In this study, the proposed algorithm has diagnosed the defects of triple components as well as single and dual components of the gas turbine engine in off-design condition.

• Journal of ILASS-Korea
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• v.7 no.3
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• pp.31-37
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• 2002

In previous study, diesel-methanol stratified injection system is manufactured and applied to a D.I. diesel engine in order to realize combustion improvement using methanol, which is oxygenated fuel with large latent heat. We know that NOx and soot is reduced by stratified injection of diesel fuel-methanol. Therefore, in the present study, combustion diagnosis using optical measurement is tried to make clear effect of methanol on simultaneous reduction of NOx and soot. Two-color method is used to measure flame temperature and KL value, which is approximately proportional to the soot consentration along the optical path. Laser induced scattering method was used to measure distribution of soot at two dimensional area. Also, it is compared exhaust characteristics of NOx and soot with results of optical measurement.