• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.1
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• pp.49-55
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• 2012

Asynchronous counters, where the counter value is changed not by a synchronizing clock but by outer inputs, are used in various modern digital systems such as spaceborne electronics. In this paper, we propose a scheme of fault tolerance for asynchronous counters with critical races caused by total ionizing dose (TID) in space. As a typical design flaw of asynchronous digital circuits, critical races cause an asynchronous circuit to show non-deterministic behavior, i.e., the next stable state of a state transition is not a fixed value but may be any value of a state set. Using the corrective control scheme for asynchronous sequential machines, this paper provides an existence condition and design procedure for a state feedback controller that can invalidate the effect of critical races. We implement the proposed control system in VHDL code and conduct experiments to demonstrate that the proposed control system can overcome critical races.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.3
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• pp.121-126
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• 2020

Sound-based machine fault diagnosis is the automatic detection of abnormal sound in the acoustic emission signals of the machines. Conventional methods of using mathematical models were difficult to diagnose machine failure due to the complexity of the industry machinery system and the existence of nonlinear factors such as noises. Therefore, we want to solve the problem of machine fault diagnosis as a deep learning-based image classification problem. In the paper, we propose a CNN-based automatic machine fault diagnosis method using Spectrogram images. The proposed method uses STFT to effectively extract feature vectors from frequencies generated by machine defects, and the feature vectors detected by STFT were converted into spectrogram images and classified by CNN by machine status. The results show that the proposed method can be effectively used not only to detect defects but also to various automatic diagnosis system based on sound.

• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.1-6
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• 2021

In the modern society, traffic problems are occurring as vehicle ownership increases. In particular, the incidence of highway traffic accidents is low, but the fatality rate is high. Therefore, a technology for detecting an abnormality in a vehicle is being studied. Among them, there is a vehicle anomaly detection technology using deep learning. This detects vehicle abnormalities such as a stopped vehicle due to an accident or engine failure. However, if an abnormality occurs on the road, it is possible to quickly respond to the driver's location. In this study, we propose a deep learning-based vehicle anomaly detection using road CCTV data. The proposed method preprocesses the road CCTV data. The pre-processing uses the background extraction algorithm MOG2 to separate the background and the foreground. The foreground refers to a vehicle with displacement, and a vehicle with an abnormality on the road is judged as a background because there is no displacement. The image that the background is extracted detects an object using YOLOv4. It is determined that the vehicle is abnormal.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.3
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• pp.450-456
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• 2016

STDR (sequence time domain reflectometry) to detect a cable fault using a pseudo noise sequence as a reference signal, and time correlation analysis between the reference signal and reflection signal is robust to noisy environments and can detect intermittent faults including open faults and short circuits. On the other hand, if the distance of the fault location is far away or the fault type is a soft fault, attenuation of the reflected signal becomes larger; hence the correlation coefficient in the STDR becomes smaller, which makes fault detection difficult and the measurement error larger. In addition, automation of the fault location by detection of phase and peak value becomes difficult. Therefore, to improve the cable fault detection of a conventional STDR, this paper proposes the algorithm in that the peak value of the correlation coefficient of the reference signal is detected, and a peak value of the correlation coefficient of the reflected signal is then detected after removing the reference signal. The performance of the proposed method was validated experimentally in low-voltage power cables. The performance evaluation showed that the proposed method can identify whether a fault occurred more accurately and can track the fault locations better than conventional STDR despite the signal attenuation. In addition, there was no error of an automatic fault type and its location by the detection of the phase and peak value through the elimination of the reference signal and normalization of the correlation coefficient.

• Journal of Korean Society for Quality Management
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• v.47 no.3
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• pp.571-582
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• 2019

Purpose: To develop a risk metric for failure cause that can help determine the action priority of each failure cause in FMEA considering time sequence of cause- failure- detection. Methods: Assuming a quadratic loss function the unfulfilled mission period, a risk metric is obtained by deriving the failure time distribution. Results: The proposed risk metric has some reasonable properties for evaluating risk accompanied with a failure cause. Conclusion: The study may be applied to determining action priorities among all the failure causes in the FMEA sheet, requiring further studies for general situation of failure process.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.27 no.3
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• pp.15-23
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• 2019

This paper contains analyses and development processes of the requirements to detect the possible intermittent failure in an old avionics backplane. Interconnections for signal transmission between electronic components, such as Pin-to-PCB, FPCB-to-FPCB, pin-to-FPCB, and pint-to-wire, were selected as the main cause of intermittent failure by analyzing target equipment and documents. The possibility of detecting intermittent failures occurring in the target equipment is verified by physics-of-failure analyses. In order to verify the occurrence of intermittent failures and their detectability, latching continuity circuit testers were manufactured and accelerated life tests were performed by applying temperature and vibration cycle in consideration of flight conditions. Through the above process, the detection requirements for the major intermittent failure in the target avionics backplane was developed.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.2
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• pp.111-120
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• 2012

Built-In-Test is a design feature in more and more advanced weapon system. During development test and evaluation(DT&E) it is critical that the BIT system be evaluated. The BIT system is an integral part of the weapon system and subsystem. Built-In-Test assists in conducting on system and subsystem failure detection and isolation to the Line Replaceable Unit(LRU). This capability reduces the need for highly skilled personnel and special test equipment at organizational level, and reduces maintenance down-time of system by shortening Total Corrective Maintenance Time. During DT&E of weapon system the objective of BIT system evaluation is to determine BIT capabilities achieved and to identify deficiencies in the BIT system. As a result corrective actions are implemented while the system is still in development. Through the use of the reiterative BIT evaluation the BIT system design was corrected, improved, or updated, as the BIT system matured.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.129-143
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• 2021

This paper proposes actuator fault detection and adaptive fault-tolerant control algorithms using performance index and human-like learning for longitudinal autonomous vehicles. Conventional longitudinal controller for autonomous driving consists of supervisory, upper level and lower level controllers. In this paper, feedback control law and PID control algorithm have been used for upper level and lower level controllers, respectively. For actuator fault-tolerant control, adaptive rule has been designed using the gradient descent method with estimated coefficients. In order to adjust the control parameter used for determination of adaptation gain, human-like learning algorithm has been designed based on perceptron learning method using control errors and control parameter. It is designed that the learning algorithm determines current control parameter by saving it in memory and updating based on the cost function-based gradient descent method. Based on the updated control parameter, the longitudinal acceleration has been computed adaptively using feedback law for actuator fault-tolerant control. The finite window-based performance index has been designed for detection and evaluation of actuator performance degradation using control error.

• The KIPS Transactions:PartA
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• v.15A no.6
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• pp.309-316
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• 2008

The mission-critical engagement control computer for air defense has to maintain its operation without any fault for a long mission time. The mission performed by large-scale and complex embedded software is extremely critical in terms of dependability and safety of computer system, and it is very important that engagement control computer has high reliability. The engagement control computer was implemented using four processors. The distributed computer composed of four processors quarantees the dependability and safety, and ASR fault-tolerant technique applied to each processor guarantees the reliability. In this paper, the mechanism and performance of ASR fault-tolerant technique are analysed. And MTBF, reliability, availability, and cost-effectiveness for ASR, DMR and TMR techniques applied to the engagement control computer are analysed. The mission-critical engagement control computer using software-based ASR fault-tolerant technique provides high reliability and fast recovery time at a low cost. The mission reliability of the engagement control computer using ASR technique in 4 processors board is almost same the reliability of the computer using TMR technique in 6 processors board. ASR technique is most suitable to the mission-critical engagement control computer.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.31-37
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• 2021

As the size of buildings increases due to urbanization due to the development of industry, the need to purify the air and maintain a comfortable indoor environment is also increasing. With the development of monitoring technology for refrigeration systems, it has become possible to manage the amount of electricity consumed in buildings. In particular, refrigeration systems account for about 40% of power consumption in commercial buildings. Therefore, in order to develop the refrigeration system failure diagnosis algorithm in this study, the purpose of this study was to understand the structure of the refrigeration system, collect and analyze data generated during the operation of the refrigeration system, and quickly detect and classify failure situations with various types and severity . In particular, in order to improve the classification accuracy of failure types that are difficult to classify, a three-step diagnosis and classification algorithm was developed and proposed. A model based on SVM and LGBM was presented as a classification model suitable for each stage after a number of experiments and hyper-parameter optimization process. In this study, the characteristics affecting failure were preserved as much as possible, and all failure types, including refrigerant-related failures, which had been difficult in previous studies, were derived with excellent results.