• Journal of IKEEE
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• v.19 no.3
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• pp.357-365
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• 2015

Recently, embedded systems such as aircraft and automobilie, are developed as modular architecture instead of federated architecture because of SWaP(Size, Weight and Power) issues. In addition, partition operating system that support multiple logical node based on partition concept were recently appeared. Distributed recovery block is fault tolerance design scheme that applicable to mission critical real-time system to support real-time take over via real-time synchronization between participated nodes. Because of real-time synchronization, single-core based computer is not suitable for partition based distributed recovery block design scheme. Multi-core and AMP(Asymmetric Multi-Processing) based partition architecture is required to apply distributed recovery block design scheme. In this paper, we proposed design scheme of distributed recovery block on the multi-core based supervised-AMP architecture partition operating system. This paper implements flight control simulator for avionics to check feasibility of our design scheme.

• Journal of Korean Association for Spatial Structures
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• v.10 no.4
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• pp.93-102
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• 2010

In this study, a smart sky-bridge composed of MR damper and FPS has been proposed and vibration control performance of a smart sky-bridge for the connected buildings was investigated. To this end, 10-story and 20-story building structures connected by a smart sky-bridge were selected as example structures and El Centro and Kobe earthquakes, which have near and far fault ground motion characteristics respectively, were used for time history analyses. In order to effectively control the smart sky-bridge, fuzzy logic controller was developed and multi-objective genetic algorithm was used to optimize fuzzy logic controllers. Based on optimization results, it has been seen that there is a trade-off between seismic responses of 10-story and 20-story buildings and a suite of Pareto optimal solutions of fuzzy logic controllers for seismic response control can be obtained by multi-objective genetic algorithm. It is shown from numerical study that seismic responses of adjacent buildings can be efficiently controlled by using a smart sky-bridge.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.1
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• pp.33-39
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• 2022

A multi-phase brushless direct current (BLDC) motor is widely used in large-capacity electric propulsion systems such as submarines and electric ships. In particular, in the field of military submarines, the polyphaser motor must suppress torque ripple in various failure situations to reduce noise and ensure stable operation for a long time. In this paper, we propose a polyphaser current control method that can improve efficiency and reduce torque ripple by minimizing the increase in stator winding loss at maximum output torque by controlling the phase angle and amplitude of the steady-state current during open circuit failure of the stator winding. The proposed control method controls the magnitude and phase angle of the healthy phase current, excluding the faulty phase, to compensate for the torque ripple that occurs in the case of a phase open failure of the motor. The magnitude and phase angle of the controlled steady-state current are calculated for each phase so that copper loss increase is minimized. The proposed control method was verified using hardware-in-the-loop simulation (HILS) of a 12-phase BLDC motor. HILS verification confirmed that the increase in the loss of the stator winding and the magnitude of the torque ripple decreased compared with the open phase fault of the motor.

• Journal of the Korea Society of Computer and Information
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• v.18 no.12
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• pp.11-19
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• 2013

This paper proposes an algorithm using vibration signals and texture analysis for mechanical fault diagnosis of an induction motor. We analyze characteristics of contrast and pattern of an image converted from vibration signal and extract three texture features using gray-level co-occurrence model(GLCM). Then, the extracted features are used as inputs of a multi-level support vector machine(MLSVM) which utilizes the radial basis function(RBF) kernel function to classify each fault type. In addition, we evaluate the classification performance with varying the parameter from 0.3 to 1.0 for the RBF kernel function of MLSVM, and the proposed algorithm achieved 100% classification accuracy with the parameter of the RBF from 0.3 to 1.0. Moreover, the proposed algorithm achieved about 98% classification accuracy with 15dB and 20dB noise inserted vibration signals.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.175-189
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• 2023

Fault detection and diagnosis (FDI) algorithms are actively being researched for ensuring the integrity and reliability of environment perception sensors in autonomous vehicles. In this paper, a fault detection algorithm based on a multi-sensor perception system composed of radar, camera, and lidar is proposed to guarantee the safety of an autonomous vehicle's perception system. The algorithm utilizes reference generation filters and residual generation filters based on finite impulse response (FIR) filter estimates. By analyzing the residuals generated from the filtered sensor observations and the estimated state errors of individual objects, the algorithm detects faults in the environment perception sensors. The proposed algorithm was evaluated by comparing its performance with a Kalman filter-based algorithm through numerical simulations in a virtual environment. This research could help to ensure the safety and reliability of autonomous vehicles and to enhance the integrity of their environment perception sensors.

• The Journal of the Acoustical Society of Korea
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• v.30 no.8
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• pp.446-460
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• 2011

The use of induction motors has been recently increasing with automation in aeronautical and automotive industries, and it playes a significant role. This has motivated that many researchers have studied on developing fault detection and classification systems of an induction motor in order to minimize economical damage caused by its fault. With this reason, this paper proposed feature vector extraction methods based on STE (short-time energy)+SVD (singular value decomposition) and DCT (discrete cosine transform)+SVD techniques to early detect and diagnose faults of induction motors, and classified faults of an induction motor into different types of them by using extracted features as inputs of BPNN (back propagation neural network) and multi-layer SVM (support vector machine). When BPNN and multi-lay SVM are used as classifiers for fault classification, there are many settings that affect classification performance: the number of input layers, the number of hidden layers and learning algorithms for BPNN, and standard deviation values of Gaussian radial basis function for multi-layer SVM. Therefore, this paper quantitatively simulated to find appropriate settings for those classifiers yielding higher classification performance than others.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.445-454
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• 2023

Recently, various fault diagnosis studies are being conducted utilizing data from collaborative robots. Existing studies performing fault diagnosis on collaborative robots use static data collected based on the assumed operation of predefined devices. Therefore, the fault diagnosis model has a limitation of increasing dependency on the learned data patterns. Additionally, there is a limitation in that a diagnosis reflecting the characteristics of collaborative robots operating with multiple joints could not be conducted due to experiments using a single motor. This paper proposes an LSTM diagnostic model that can overcome these two limitations. The proposed method selects representative normal patterns using the correlation analysis of vibration and current data in single-axis and multi-axis work environments, and generates residual patterns through differences from the normal representative patterns. An LSTM model that can perform gear wear diagnosis for each axis is created using the generated residual patterns as inputs. This fault diagnosis model can not only reduce the dependence on the model's learning data patterns through representative patterns for each operation, but also diagnose faults occurring during multi-axis operation. Finally, reflecting both internal and external data characteristics, the fault diagnosis performance was improved, showing a high diagnostic performance of 98.57%.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.12
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• pp.2072-2077
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• 2007

The primary objective of all power systems is to maintain the reliability and to minimize outage time for fault or the others. The frequency relaying algorithm perceives a variation of system frequency and thereafter detects the unbalance between generation and load. A multi-agent system is composed of multiple interacting computing elements that are known as agents. In this paper, frequency relaying algorithm is designed by multi-agent system and is implemented by EMTP-MODELS. To verify performance of the frequency relaying algorithm based on multi-agent system, simulations by EMTP have been carried out.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.401-406
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• 2006

This paper presents the fault diagnosis of induction motor based on support vector machine(SVMs). SVMs are well known as intelligent classifier with strong generalization ability. Application SVMs using kernel function is widely used for multi-class classification procedure. In this paper, the algorithm of SVMs will be combined with feature extraction and reduction using component analysis such as independent component analysis, principal component analysis and their kernel(KICA and KPCA). According to the result, component analysis is very useful to extract the useful features and to reduce the dimensionality of features so that the classification procedure in SVM can perform well. Moreover, this method is used to induction motor for faults detection based on vibration and current signals. The results show that this method can well classify and separate each condition of faults in induction motor based on experimental work.

• Structural Engineering and Mechanics
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• v.40 no.4
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• pp.489-500
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• 2011

The elastic seismic response of plan-asymmetric multi storey steel-frame buildings is investigated under earthquake loading with particular emphasis on forward-rupture directivity and fling records. Three asymmetric building systems are generated with different torsional stiffness and varying static eccentricity. The structural characteristic of these systems are designed according to UBC 97 code and their seismic responses subjected to a set of earthquake records are obtained from the response history analysis (RHA) as well as the linear static analysis (LSA). It is shown that, the elastic torsional response is influenced by the intensity of near-fault ground motions with different energy contents. In the extreme case of very strong earthquakes, the behaviour of torsionally stiff buildings and torsionally flexible buildings may differ substantially due to the fact that the displacement envelope of the deck depends on ground motion characteristics.