• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.1
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• pp.47-57
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• 1992

Subject of this work is the application of neural networks for the signal(motor noise)recognition systems which detects motor failures and employs different signal(noise). Charaoteristics that re-sult from damaghe part and measure of motor construction during working. The four layers neural networks is applied to this examination. And consists of one input layer, two hidden layers, and one output layer, and learns by the back propagation algorithm.The results of this examination show that it the construction and the output power of the testmotor and learning motor are compatible, the damaged part of the testmotor are detected correctly in the system on the other hand, if the motors have different constrcotion but similar output power each other, mislesding results are obtained in this system.

• Journal of Aerospace System Engineering
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• v.11 no.1
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• pp.35-40
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• 2017

Aircraft diagnostic systems identify system failures and nip aircraft accidents in the bud by removing hazards that hinder Safety Of Flight (SOF). The necessity for diagnostic systems is increasing as aircraft manufacturing technology is modernized. Many countries have conducted studies and developed diagnostic systems. However, studies about diagnostic systems are very few in Korea. This study defines the scope of aircraft diagnostics systems and closely considers methods to ensure the Safety Of Flight (SOF) for military aircraft.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.308-313
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• 2016

Modern systems have become more and more complex due to the ever-increasing user requirements and rapid advance of technology. As such, the frequency of accidents due to system design errors or failure has been increasing. When the damage incurred by accidents to human beings or property is serious, the underlying systems are referred to as safety-critical systems. The development of such systems requires special efforts to ensure the safety of the human beings operating them. To cope with such a requirement, in this paper an approach is employed in which we consider safety starting from the conceptual design phase of the systems. Specifically, a systems design method that can detect functional failure is proposed by utilizing meta-models and M&S methods. To accomplish this, the safety design data from international safety standards are first extracted and also a meta-model is generated using SysML (systems modeling language). Then, a SysML-based system design method is proposed based on the use of the developed meta-model. We also discuss how the safety requirements can be created and verified using a simulation method. Finally, through a case study in automotive design, it is demonstrated that the detection of a functional failure and the verification of a safety requirement can be accomplished using the SysML-based M&S method. This study indicates that the use of meta-models can be useful for collecting and managing safety data and that the meta-model based M&S method can make it possible to satisfy the system requirements by reducing the design errors.

• The Journal of Bigdata
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• v.7 no.1
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• pp.125-137
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• 2022

The transportation industry is one of the important industries due to the geographical requirements surrounded by the sea on three sides of Korea and the problem of resource poverty, which relies on imports for most of its resource consumption. Among them, the proportion of the shipping industry is large enough to account for most of the transportation industry, and maintenance in the shipping industry is also important in improving the operational efficiency and reducing costs of ships. However, currently, inspections are conducted every certain period of time for maintenance of ships, resulting in time and cost, and the cause is not properly identified. Therefore, in this study, the proposed methodology, LSTM-AutoEncoder, is used to detect abnormalities that may cause ship failure by considering the time of actual ship operation data. In addition, clustering is performed through clustering, and the potential causes of ship main engine failure are identified by grouping outlier by factor. This enables faster monitoring of various information on the ship and identifies the degree of abnormality. In addition, the current ship's fault monitoring system will be equipped with a concrete alarm point setting and a fault diagnosis system, and it will be able to help find the maintenance time.

• Journal of Advanced Marine Engineering and Technology
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• v.31 no.6
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• pp.791-796
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• 2007

When 2 sets of PID controllers are coupled directly each other to configure a closed control loop on behalf of coupling a controller and a plant. the behaviors or this exclusive loop system are expected to be unique in inherent system responses. If its properties be disclosed and generalized well in advance, it is possible for us to use the results for the purpose of fault detection and performance monitoring between control stations from the stage of system design. particularly in such cases as cascade control systems. In this paper. general properties of the proposed system are analyzed firstly to check whether it is controllable and how its steady responses would be. To simplify calculation, the analysis has been performed based on the transfer equation derived from a modelled case which consists of 2 PI controllers and signal converters between them. including time delay element and first-lag element to consider the situation of signal transmission. The results acquired from simulation are suggested to show how it works actually.

• Journal of IKEEE
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• v.24 no.3
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• pp.727-734
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• 2020

Fault tolerant corrective control for asynchronous sequential machines (ASMs) with transient faults is discussed in this paper. The considered ASM is vulnerable to a kind of faults whose manifestation may arise during transient transitions of the ASM, leading to transient faults occurring in non-fundamental mode. To overcome adverse effects caused by these faults, we present a novel corrective control scheme that can detect and tolerate transient faults in non-fundamental mode. The existence condition and design algorithm for an appropriate fault tolerant controller is addressed in the framework of corrective control theory. The applicability of the proposed control methodology is demonstrated in the FPGA experiment.

• The Journal of the Acoustical Society of Korea
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• v.28 no.8
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• pp.822-831
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• 2009

The porcelain insulators are important devices, that are used to isolate electrically and hold mechanically in the high-voltage power transmission systems. The faults of the insulators induce very serious problems to the power transmission line. In this paper, we introduce techniques for fault detections of insulators by acoustic radiation noises from them. We measured radiation noises from normal state insulators and fault state insulators. The used insulators were two different type porcelain insulators, a cut out switch, two different type line posters, and a lightning arrester. Each results was compared each other in time domain, frequency domain and filter banks' outputs. We found the possibility of detection of insulators' faults and also suggested techniques for fault detections.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.3
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• pp.399-408
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• 2022

This paper proposes a corrective control system to achieve fault-tolerant control for input/output asynchronous sequential circuits vulnerable to transient faults violating fundamental mode operations. To overcome non-fundamental mode faults occurring in transient transitions of asynchronous sequential circuits, it is necessary to determine the end of unauthorized state transitions caused by the faults and to stably take the circuit from the faulty state to a desired state that is output equivalent with the normal next stable state. We address the existence condition for a proper output-feedback corrective controller that achieves fault diagnosis and fault-tolerant control for these non-fundamental mode faults. The corrective controller and asynchronous sequential circuit are implemented on field-programming gate array to demonstrate the synthesis procedure and applicability of the proposed control scheme.

• Advanced Industrial SCIence
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• v.2 no.4
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• pp.11-16
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• 2023

The subway is a means of public transportation that plays an important role in the transportation system of modern cities. However, congestion often occurs due to sudden breakdowns and system outages, causing inconvenience. Therefore, in this paper, we conducted a study on failure prediction and prevention using machine learning to efficiently operate the subway system. Using UC Irvine's MetroPT-3 dataset, we built a subway breakdown prediction model using logistic regression. The model predicted the non-failure state with a high accuracy of 0.991. However, precision and recall are relatively low, suggesting the possibility of error in failure prediction. The ROC_AUC value is 0.901, indicating that the model can classify better than random guessing. The constructed model is useful for stable operation of the subway system, but additional research is needed to improve performance. Therefore, in the future, if there is a lot of learning data and the data is well purified, failure can be prevented by pre-inspection through prediction.

• The Transactions of the Korea Information Processing Society
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• v.13 no.3
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• pp.130-139
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• 2024

Artificial intelligence models are being used to detect facility anomalies using physics data such as vibration, current, and temperature for predictive maintenance in the manufacturing industry. Since the types of facility anomalies, such as facility defects and failures, anomaly detection methods using autoencoder-based unsupervised learning models have been mainly applied. Normal or abnormal facility conditions can be effectively classified using the reconstruction error of the autoencoder, but there is a limit to identifying facility anomalies specifically. When facility anomalies such as unbalance, misalignment, and looseness occur, the facility vibration frequency shows a pattern different from the normal state in a specific frequency range. This paper presents an N-segmentation anomaly detection method that performs anomaly detection by dividing the entire vibration frequency range into N regions. Experiments on nine kinds of anomaly data with different frequencies and amplitudes using vibration data from a compressor showed better performance when N-segmentation was applied. The proposed method helps materialize them after detecting facility anomalies.