• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.12
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• pp.1177-1183
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• 2010

In this paper, a parallel bank of multiple adaptive unknown input observers approach suggested by D.Wang is applied to detect a single fault of control surface actuator and to estimate the actuator position of lock-in-place fault using a small fixed-wing UAV model with eight control surfaces. This paper shows that not only the fault diagnosis algorithm detects and estimates each faults of lock-in-place in 1 second by simulation but also it may be unavailable to isolate among two same-shaped rudders.

• Nuclear Engineering and Technology
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• v.27 no.5
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• pp.670-686
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• 1995

This paper provides an improvement on our previous study ［1］ for multi-fault diagnosis in real time in large-scale systems. In the method, fault propagation probability(FPP) and fault propagation time(FPT) in a fuzzy sense are additively used to describe the fault propagation model(FPM) in more practical manner. A modified fault diagnosis procedure is also given. This method is applied for diagnosis of the primary system in the Kori nuclear power plant unit 2 under a transient condition in case of unit value of FPP on each branch of the FPM.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.89-96
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• 2018

When a fault occurs in a power system, the existing analytic models for the power system fault diagnosis could generate multiple solutions under the condition of one or more protective relays (PRs) and/or circuit breakers (CBs) malfunctioning, and/or an alarm or alarms of these PRs and/or CBs failing. Therefore, this paper presents an improved analytic model addressing the above problem. It takes into account the interaction between the uncertainty involved with PR operation and CB tripping and the uncertainty of the alarm reception, which makes the analytic model more reasonable. In addition, the existing analytic models apply the penalty function method to deal with constraints, which is influenced by the artificial setting of the penalty factor. In order to avoid the penalty factor's effects, this paper transforms constraints into an objective function, and then puts forward an improved immune clonal multi-objective optimization algorithm to solve the optimal solution. Finally, the cases of the power system fault diagnosis are served for demonstrating the feasibility and efficiency of the proposed model and method.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.348-358
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• 2022

Software fault prediction is a method to compute fault in the software sections using software properties which helps to evaluate the quality of software in terms of cost and effort. Recently, several software fault detection techniques have been proposed to classifying faulty or non-faulty. However, for such a person, and most studies have shown the power of predictive errors in their own databases, the performance of the software is not consistent. In this paper, we propose a hybrid soft computing technique for SFP based on optimal feature extraction and classification (HST-SFP). First, we introduce the bat induced butterfly optimization (BBO) algorithm for optimal feature selection among multiple features which compute the most optimal features and remove unnecessary features. Second, we develop a layered recurrent neural network (L-RNN) based classifier for predict the software faults based on their features which enhance the detection accuracy. Finally, the proposed HST-SFP technique has the more effectiveness in some sophisticated technical terms that outperform databases of probability of detection, accuracy, probability of false alarms, precision, ROC, F measure and AUC.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.119-130
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• 2023

Among the various elements of probabilistic safety assessment (PSA), human failure events (HFEs) and their dependencies are major contributors to the quantification of risk of a nuclear power plant. Currently, the dependency among HFEs is reflected using a post-processing method in PSA, wherein several drawbacks, such as limited propagation of minimal cutsets through the fault tree and improper truncation of minimal cutsets exist. In this paper, we propose a method to model the HFE dependency directly in a fault tree using the if-then-else logic. The proposed method proved to be equivalent to the conventional post-processing method while addressing the drawbacks of the latter. We also developed a software tool to facilitate the implementation of the proposed method considering the need for modeling the dependency between multiple HFEs. We applied the proposed method to a specific case to demonstrate the drawbacks of the conventional post-processing method and the advantages of the proposed method. When applied appropriately under specific conditions, the direct fault-tree modeling of HFE dependency enhances the accuracy of the risk quantification and facilitates the analysis of minimal cutsets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1186-1207
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• 2024

Bearings are one of the main components of mechanical equipment and one of the primary components prone to faults. Therefore, conducting fault diagnosis on bearings is a key issue in mechanical equipment research. Belief rule base (BRB) is essentially an expert system that effectively integrates qualitative and quantitative information, demonstrating excellent performance in fault diagnosis. However, class imbalance often occurs in the diagnosis task, which poses challenges to the diagnosis. Models with interpretability can enhance decision-makers' trust in the output results. However, the randomness in the optimization process can undermine interpretability, thereby reducing the level of trustworthiness in the results. Therefore, a hierarchical BRB model based on extreme gradient boosting (XGBoost) feature selection with interpretability (HFS-IBRB) is proposed in this paper. Utilizing a main BRB alongside multiple sub-BRBs allows for the conversion of a multi-classification challenge into several distinct binary classification tasks, thereby leading to enhanced accuracy. By incorporating interpretability constraints into the model, interpretability is effectively ensured. Finally, the case study of the actual dataset of bearing fault diagnosis demonstrates the ability of the HFS-IBRB model to perform accurate and interpretable diagnosis.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.11
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• pp.50-55
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• 2008

As more DSP functionalities are integrated into an embedded mobile device, power consumption and device reliability have emerged as crucial issues. As the complexity of mobile embedded designs increases very rapidly, verifying the functionality of the mobile devices has become extremely difficult. Therefore, designs with error (fault) tolerance are often required since these capabilities will enable the design to operate properly even with some existence of errors. However, designs with fault tolerance may suffer from significant power overhead since fault tolerance is often achieved by resource replication. In this paper, we propose a low power and fault tolerant MAC (multiply-and-accumulate) design. The proposed MAC design is based on multiple barrel shifters since MAC designs with barrel-shifters and adders are known to be excellent in terms of power consumption.

• The Journal of the Acoustical Society of Korea
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• v.15 no.5
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• pp.73-81
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• 1996

In multiprocessor systems with multiple processors and memories, efficient communication between processors and memories is critical for high performance. Various types of multistage networks have been proposed. The economic feasibility and the improvements in both computing throughput and fault tolerance/diagnosis have been some of the most important factors in the development of these computer systems. In this paper, we present an efficient algorithm for the diagnosis of generalized cube interconnection networks with a fan-in/fan-out of 2. Also, using the assumed fault model present total fault diagnosis by generating suitable fault-detection and fault-location test sets for link stuck fault, switching element fault in direct/cross states, including broadcast diagnosis methods based on some basic properties or generalized cube interconnection networks. Finally, we illustrate some example.

• Journal of KIISE:Information Networking
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• v.31 no.3
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• pp.335-346
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• 2004

In this paper, we present a high-performance fault-tolerant switching networks using a deflection self-routing scheme, and present fault-diagnosis method for the network. We use the facts: 1) Each stage of the Banyan network is arrayed as the sequences of a Cyclic group of SEs. 2) There is the homomorphism between adjacent stages from a view of self-routing, so that all of each Cyclic group is the subgroup of the Cyclic group in the next stage, and there are factor groups due to such subgroup and homomorphism. We provide high-performance fault-tolerant switching networks of which the all links including augmented links are used as the alternate links detouring faulty links. We also present the fault diagnosis scheme for the proposed switching network that provide multiple paths for each input-output pair.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.2
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• pp.283-288
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• 2015

The possible erroneous output data of the sensor nodes can cause the performance limit or the degradation of the reliability in the whole wireless sensor networks(WSN). In this paper, we propose a new sensor node scheme with multiple sensors and a new fault diagnostic algorithm. The algorithm can increase the reliability of the whole WSNs by utilizing measurements of the multiple sensors on the node and by determining the validity of the date by comparing the value of each sensor. It can increase the cost and complexity of the node, but is suitable for the area where the high reliability is critical.