• International Journal of Aeronautical and Space Sciences
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• v.16 no.1
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• pp.89-101
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• 2015

To satisfy civil aviation requirements using the Global Navigation Satellite System (GNSS), it is important to guarantee system integrity. In this work, we propose a fault detection algorithm for GNSS ephemeris anomalies. The basic principle concerns baseline length estimation with GNSS measurements (pseudorange, broadcasted ephemerides). The estimated baseline length is subtracted from the true baseline length, computed using the exact surveyed ground antenna positions. If this subtracted value differs by more than a given threshold, this indicates that an ephemeris anomaly has been detected. This algorithm is suitable for detecting Type A ephemeris failure, and more advantageous for use with multiple stations with various long baseline vectors. The principles of the algorithm, sensitivity analysis, minimum detectable error (MDE), and protection level derivation are described and we verify the sensitivity analysis and algorithm availability based on real GPS data in Korea. Consequently, this algorithm is appropriate for GNSS regional implementation.

• International Journal of Aeronautical and Space Sciences
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• v.9 no.2
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• pp.64-70
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• 2008

The UAV propulsion system that will be operated for long time at more than 40,000ft altitude should have not only fuel flow minimization but also high reliability and durability. If this UAV propulsion system may have faults, it is not easy to recover the system from the abnormal, and hence an accurate diagnostic technology must be needed to keep the operational reliability. For this purpose, the development of the health monitoring system which can monitor remotely the engine condition should be required. In this study, a fuzzy trend monitoring method for detecting the engine faults including mechanical faults was proposed through analyzing performance trends of measurement data. The trend monitoring is an engine conditioning method which can find engine faults by monitoring important measuring parameters such as fuel flow, exhaust gas temperatures, rotational speeds, vibration and etc. Using engine condition database as an input to be generated by linear regression analysis of real engine instrument data, an application of the fuzzy logic in diagnostics estimated the cause of fault in each component. According to study results. it was confirmed that the proposed trend monitoring method can improve reliability and durability of the propulsion system for a long endurance UAV to be operated at medium altitude.

• 한국지구물리탐사학회:학술대회논문집
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• 2007.06a
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• pp.185-190
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• 2007

In this study, we developed the 2.5D EM modeling and inversion algorithm for cross-hole source and receiver geometry. Considering the cross-hole environment, we use a VMD (vertical magnetic dipole) as a source and vertical magnetic fields as a measuring data. Developed inversion algorithm is tested for the isolated block model which has a conductive and a resistivity anomaly respectively. For the conductive anomaly, its size and resistivity are inverted well on the inversion results, while for the resistive anomaly, the location of anomalous block is shown on the inverted section, but its values are far from the exact value. Furthermore, artificial conductive anomalies are shown around the resistive anomalous zone. If we consider the inversion artifact shown in the test inversion of restive block, it is almost impossible to image the resistive zone. However, the main target of EM tomography in the engineering problem is conductive target such as fault zone, and contaminated zone etc., EM tomography algorithm can be used for detecting the anomalous zone.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.17 no.8
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• pp.780-788
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• 2005

Two different types of algorithms were developed and applied to detect the partial faults of a multi-type air conditioning system. Partial faults include the compressor valve leakage, the refrigerant pipe partial blockage, the condenser fouling, and the evaporator fouling. The first algorithm was developed by using mathematical models and parity relations, and the second algorithm was developed by using mathematical models and a RBF neural network. Test results showed that the second algorithm was better than the first algorithm in detecting various partial faults of the system. Therefore, the algorithm developed by using mathematical models and a RBF neural network may be used for the detection of partial faults of an air-conditioning system.

• Journal of Power Electronics
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• v.15 no.4
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• pp.1066-1073
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• 2015

This paper proposes a control strategy based on the frequency detection method, comprising a current control and a feed-forward voltage control loop, is proposed for grid-interactive power conditioning systems (PCS). For continuous provision of power to critical loads, PCS should be able to check grid outages instantaneously. Hence, proposed in the present paper are a frequency detection method for detecting abnormal grid conditions and a controller, which consists of a current controller and a feedforward voltage controller, for different operation modes. The frequency detection method can detect abnormal grid conditions accurately and quickly. The controller which has current and voltage control loops rapidly helps in load voltage regulation when grid fault occurs by changing reference and control modes. The proposed seamless transfer control strategy is confirmed by experimental results.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.20 no.6
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• pp.2057-2066
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• 1996

^In this paper, diagnostic technique for detecting the engine faults, especially misfire, are introduced and compared with each other under the same conditions. With all of them the instantaneous angular velocitys, measured at the flywheel, were analyzed. The techniques include the frequency analysis, auto-correlation function, velocity index, acceleration index, maximum acceleration index, and integrated torque index. Since the main driving components for the angular velocity fluctuation are both the pressure and the inertia torque, the component of the inertia torque in it must be excluded to extract the information of the combustion from the angular velocity. To do this, it is required to consider only the first half of the combustion period in the angular velocity fluctuations, which has never been proposed in the existing methods. On the basis of this fact, the results show that the most effective diagnostic technique is maximum acceleration index.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.12
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• pp.3081-3099
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• 2012

We propose a new multipath-based reliable routing protocol on MANETs, Multipath-based Reliable routing protocol with Fast-Recovery of failures (MRFR). For reliable message transmission, MRFR tries to find the most reliable path between a source and a destination considering the end-to-end packet reception reliability of the routes. The established path consists of a primary path that is used to transmit messages, and the secondary paths that are used to recover the path when detecting failures on the primary path. After establishing the path, the source transmits messages through the primary path. If a node detects a link failure during message transmission, it can recover the path locally by switching from the primary to the secondary path. By allowing the intermediate nodes to locally recover the route failure, the proposed protocol can handle the dynamic topological change of the MANETs efficiently. The simulation result using the QualNet simulator shows that the MRFR protocol performs better than other protocols in terms of the end-to-end message delivery ratio and fault-tolerance capability.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.84-88
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• 2008

The UAV propulsion system that will be operated for long time at more than 40,000ft altitude should have not only fuel flow minimization but also high reliability and durability. If this UAV propulsion system may have faults, it is not easy to recover the system from the abnormal, and hence an accurate diagnostic technology must be needed to keep the operational reliability. For this purpose, the development of the health monitoring system which can monitor remotely the engine condition should be required. In this study, a fuzzy trend monitoring method for detecting the engine faults including mechanical faults was proposed through analyzing performance trends of measurement data. The trend monitoring is an engine conditioning method which can find engine faults by monitoring important measuring parameters such as fuel flow, exhaust gas temperatures, rotational speeds, vibration and etc. Using engine condition database as an input to be generated by linear regression analysis of real engine instrument data, an application of the fuzzy logic in diagnostics estimated the cause of fault in each component. According to study results, it was confirmed that the proposed trend monitoring method can improve reliability and durability of the propulsion system for a long endurance UAV to be operated at medium altitude.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.611-617
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• 2018

The application of health monitoring, including a fault detection technique, is needed to secure the structural safety of large structures. A 2-step crack identification method for detecting the crack location and size of the beam structure is presented. First, a crack occurrence region was estimated using the modified Laplacian operator for the strain mode shape obtained from the distributed local strain data. The crack location and size were then identified based on the natural frequencies obtained from the acceleration data and the neural network technique for the pre-estimated crack occurrence region. The natural frequencies of a cracked beam were calculated based on an equivalent bending stiffness induced by the energy method, and used to generate the training patterns of the neural network. An experimental study was carried out on an aluminum cantilever beam to verify the present method for crack identification. Cracks were produced on the beam, and free vibration tests were performed. A crack occurrence region was estimated using the modified Laplacian operator for the strain mode shape, and the crack location and size were assessed using the natural frequencies and neural network technique. The identified crack occurrence region agrees well with the exact one, and the accuracy of the estimation results for the crack location and size could be enhanced considerably for 3 damage cases. The presented method could be applied effectively to the structural health monitoring of large structures.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.125-137
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• 2009

Acoustic emission-based techniques are being used for the nondestructive inspection of mechanical systems used in service. For reliable fault monitoring related to the crack growth, it is important to identify the dynamical characteristics as well as transient crack-related signals. Widely used methods which are based on physical phenomena of the three damage stages for detecting the crack growth have a problem that crack-related acoustic emission activities overlap in time, therefore it is insufficient to estimate the exact crack growth time. The proposed pattern recognition method uses the dynamical characteristics of acoustic emission as inputs for minimizing false alarms and miss alarms and performs the temporal clustering to estimate the crack growth time accurately. Experimental results show that the proposed method is effective for practical use because of its robustness to changes of acoustic emission caused by changes of pressure levels.