• The KIPS Transactions:PartC
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• v.9C no.4
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• pp.497-504
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• 2002

This paper proposes fault diagnosis and correction algorithms using agent collaboration, and a management model for managing network configuration faults. This management model is composed of three processes-fault detection, fault diagnosis and fault correction. Each process, based on RBR, operates on using rules which are consisted in Rule-based Knowledge Database. Proposed algorithm selves the complex fault problem that a system could not work out by itself, using agent collaboration. And the algorithm does efficiently diagnose and correct network configuration faults in abnormal network states.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.11
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• pp.968-973
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• 2000

Fault diagnosis plays an important role in the performance and safe operation of many modern engineering plants. This paper investigates the problem of fault detection using neural networks in dynamic systems. A general framework for constructing a nonlinear fault detection scheme for nonlinear dynamic systems containing modeling uncertaintly is proposed. The main idea behind the proposed approach is to monitor the physical system with an off -line learning neural network and then to approximate the upper and lower thresholds of acceleration of the nominal system with the model-based threshold(ThMB) method, The performance of the proposed fault detection scheme is investigated through simulations of a pendulum with uncertainty.

• Structural Engineering and Mechanics
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• v.92 no.2
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• pp.121-131
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• 2024

This paper proposes a fault detection method for blade pitch systems of floating wind turbines using transformer-based deep-learning models. Transformers leverage self-attention mechanisms, efficiently process time-series data, and capture long-term dependencies more effectively than traditional recurrent neural networks (RNNs). The model was trained using normal operational data to detect anomalies through high reconstruction losses when encountering abnormal data. In this study, various fault conditions in a blade pitch system, including environmental load cases, were simulated using a detailed model of a spar-type floating wind turbine, the data collected from these simulations were used to train and test the transformer models. The model demonstrated superior fault-detection capabilities with high accuracy, precision, recall, and F1 scores. The results show that the proposed method successfully identifies faults and achieves high-performance metrics, outperforming existing traditional multi-layer perceptron (MLP) models and long short-term memory-autoencoder (LSTM-AE) models. This study highlights the potential of transformer models for real-time fault detection in wind turbines, contributing to more advanced condition-monitoring systems with minimal human intervention.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.932-951
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• 2021

The effectiveness of testing in Model-based Testing (MBT) for Software Product Line (SPL) can be achieved by considering fault detection in test case. The lack of fault consideration caused test case in test suite to be listed randomly. Test Case Prioritization (TCP) is one of regression techniques that is adaptively capable to detect faults as early as possible by reordering test cases based on fault detection rate. However, there is a lack of studies that measured faults in MBT for SPL. This paper proposes a Test Case Prioritization (TCP) approach based on dissimilarity and string based distance called Last Minimal for Local Maximal Distance (LM-LMD) with Dice-Jaro-Winkler Dissimilarity. LM-LMD with Dice-Jaro-Winkler Dissimilarity adopts Local Maximum Distance as the prioritization algorithm and Dice-Jaro-Winkler similarity measure to evaluate distance among test cases. This work is based on the test case generated from statechart in Software Product Line (SPL) domain context. Our results are promising as LM-LMD with Dice-Jaro-Winkler Dissimilarity outperformed the original Local Maximum Distance, Global Maximum Distance and Enhanced All-yes Configuration algorithm in terms of Average Fault Detection Rate (APFD) and average prioritization time.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.4
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• pp.163-168
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• 2002

This paper present a new verb efficient numerical algorithm for arcing faults detection and fault distance estimation in transmission line. It is based on the fundamental differential equations describing the transients on a transmission line before, during and alter the fault occurrence, and on the application of the "Least Error Squares Technique"for the unknown model parameter estimation. If the arc voltage estimated is a near zero, the fault is without arc, in other words the fault is permanent fault. If the arc voltage estimated has any high value, the faust is identified as an fault, or the transient fault. In permanent faults case, fault distance estimation is necessary. This paper uses the model of the arcing fault in transmission line using ZnO arrestor and resistance to be implemented within EMTP. One purpose of this study is to build a structure for modeling of arcing fault detection and fault distance estimation algorithm using Matlab programming. In this paper, This algorithm has been designed in Graphic user interface(GUI).

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.7
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• pp.726-735
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• 2009

In this paper, we propose an approach to signal model-based fault detection and diagnosis system for induction motors. The current fault detection techniques used in the industry are limit checking techniques, which are simple but cannot predict the types of faults and the initiation of the faults. The system consists of two consecutive processes: fault detection process and fault diagnosis process. In the fault detection process, the system extracts the significant features from sound signals using combination of variance, cross-correlation and wavelet. Consequently, the pattern classification technique is applied to the fault diagnosis process to recognize the system faults based on faulty symptoms. The sounds generated from different kinds of typical motor's faults such as motor unbalance, bearing misalignment and bearing loose are examined. We propose two approaches for fault detection and diagnosis system that are waveletand-variance-based and wavelet-and-crosscorrelation-based approaches. The results of our experiment show more than 95 and 78 percent accuracy for fault classification, respectively.

• Journal of Hydrogen and New Energy
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• v.30 no.6
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• pp.556-566
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• 2019

Recently, as the supply of residential polymer electrolyte membrane fuel cells (PEMFCs) increases, the durability and lifetime of the PEMFC system are becoming important. The related studies have been mainly focused on the durability and lifetime of materials while the research on the durability and maintenance of the system level is insufficient. In this paper, a model-based fault detection method is developed considering an air supply system that is dominant to the system performance and efficiency. A commercial 1 kW residential fuel cell system is built, and experiments are conducted under various operation loads and states (normal, 6 faults). From the experimental data, nominal models and residuals are generated. With the residual pattern obtained from real-time data, the detection and classification of various faults can be possible. The technical importance of this paper is to minimize extra sensor installation by using the empirical model rather than a complex mathematical model, and to decrease the number of models by using the applicable model at three loads. Finally, the model-based fault detection method for the air supply system of a PEMFC is established and is expected to be applicable to other subsystems.

• Smart Structures and Systems
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• v.17 no.6
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• pp.1031-1053
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• 2016

The health conditions of in-service civil infrastructures can be evaluated by employing structural health monitoring technology. A reliable health evaluation result depends heavily on the quality of the data collected from the structural monitoring sensor network. Hence, the problem of sensor fault diagnosis has gained considerable attention in recent years. In this paper, an innovative sensor fault diagnosis method that focuses on fault detection and isolation stages has been proposed. The dynamic or auto-regressive characteristic is firstly utilized to build a multivariable statistical model that measures the correlations of the currently collected structural responses and the future possible ones in combination with the canonical correlation analysis. Two different fault detection statistics are then defined based on the above multivariable statistical model for deciding whether a fault or failure occurred in the sensor network. After that, two corresponding fault isolation indices are deduced through the contribution analysis methodology to identify the faulty sensor. Case studies, using a benchmark structure developed for bridge health monitoring, are considered in the research and demonstrate the superiority of the new proposed sensor fault diagnosis method over the traditional principal component analysis-based and the dynamic principal component analysis-based methods.

• Journal of Drive and Control
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• v.14 no.2
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• pp.30-36
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• 2017

This paper presents a sliding mode observer-based fault detection algorithm for steering inputs of an all-terrain crane. All-terrain cranes with multi-axles have several steering modes for various working purposes. Since steering angles at the other axles except the first wheel are controlled by using the information of steering angle at the first wheel, a reliable signal of the first axle's steering angle should be secured for the driving safety of cranes. For the fault detection of steering input signal, a simplified crane model-based sliding mode observer has been used. Using a sliding mode observer with an equivalent output injection signal that represents an actual fault signal, a fault signal in steering input was reconstructed. The road steering mode of the crane's steering system was used to conduct performance evaluations of a proposed algorithm, and an arbitrary fault signal was applied to the steering angle at the first wheel. Since the road steering mode has different steering strategies according to different speed intervals, performance evaluations were conducted based on the curved path scenario with various speed conditions. The design of algorithms and performance evaluations were conducted on Matlab/Simulink environment, and evaluation results reveal that the proposed algorithm is capable of detecting and reconstructing a fault signal reasonably well.

• Advances in robotics research
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• v.2 no.1
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• pp.99-112
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• 2018

The main contribution of this work is the design of a field programmable gate array (FPGA) based ARX-Laguerre proportional-integral observation (PIO) system for fault detection and identification (FDI) in a multi-input, multi-output (MIMO) nonlinear uncertain dynamical robot manipulators. An ARX-Laguerre method was used in this study to dynamic modeling the robot manipulator in the presence of uncertainty and disturbance. To address the challenges of robustness, fault detection, isolation, and estimation the proposed FPGA-based PI observer was applied to the ARX-Laguerre robot model. The effectiveness and accuracy of FPGA based ARX-Laguerre PIO was tested by first three degrees of the freedom PUMA robot manipulator, yielding 6.3%, 10.73%, and 4.23%, average performance improvement for three types of faults (e.g., actuator fault, sensor faults, and composite fault), respectively.