• Journal of KIISE:Computer Systems and Theory
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• v.33 no.1_2
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• pp.105-109
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• 2006

Hypercube has a regular and hierarchical structure, therefore it can be applied to the development of efficient diagnosis algorithm. Kranakis and Pelc [7] have proposed HYP-DIAG algorithm to implement different method of HADA/IHADA and adaptive cube partition method after embedding the small size of ring that includes all the faulty nodes. In this paper, we propose new method to reduce testing rounds by analyzing the syndrome of RGC-rings gained in the first step of HYP-DIAG and analyze the proposed algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.3
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• pp.244-251
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• 1997

In this paper, we propose a fault diagnosis algorithm to detect and isolate multiple faults in a system. The proposed fault diagnosis algorithm is based on a multiple fault classifier which consists of two ART2 NN(adaptive resonance theory2 neural network) modules and the algorithm is composed of three main parts - parameter estimation, fault detection and isolation. When a change in the system occurs, estimated parameters go through a transition zone in which residuals between the system output and the estimated output cross the threshold, and in this zone, estimated parameters are transferred to the multiple faults classifier for fault isolation. From the computer simulation results, it is verified that when the proposed diagnosis algorithm is performed successfully, it detects and isolates faults in the position control system of a DC motor.

• Journal of Power Electronics
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• v.16 no.3
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• pp.1012-1024
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• 2016

This study deals with an on-line software fault detection, localization, and system reconfiguration method for electrical system drives composed of three-phase AC/DC/AC converters and three-phase permanent magnet synchronous machine (PMSM) drives. Current sensor failure (outage), speed/position sensor loss (disconnection), and damaged DC-link voltage sensor are considered faults. The occurrence of these faults in PMSM drive systems degrades system performance and affects the safety, maintenance, and service continuity of the electrical system drives. The proposed method is based on the monitoring signals of "abc" currents, DC-link voltage, and rotor speed/position using a measurement chain. The listed signals are analyzed and evaluated with the generated residuals and threshold values obtained from a Sliding Mode Current-Speed-DC-link Voltage Observer (SMCSVO) to acquire an on-line fault decision. The novelty of the method is the faults diagnosis algorithm that combines the use of SMCSVO and adaptive thresholds; thus, the number of false alarms is reduced, and the reliability and robustness of the fault detection system are guaranteed. Furthermore, the proposed algorithm's performance is experimentally analyzed and tested in real time using a dSPACE DS 1104 digital signal processor board.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10a
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• pp.836-841
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• 1987

Many kinds of conditioning monitoring technique have been studied, so this study has investigated the possibility of checking the trend in the fault diagnosis of ball bearing, one of the elements of rotating machine, by applying the cepstral analysis method using the adaptive noise cancelling (ANC) method. And computer simulation is conducted in oder to identify obviously the physical meaning of ANC. The optimal adaptation gain in adaptive filter is estimated, the performance of ANC according to the change of the signal to noise ratio and convergence of LMS algorithm is considered by simulation. It is verified that cepstral analysis using ANC method is more effective than the conventional cepstral analysis method in bearing fault diagnosis.

• Journal of Electrical Engineering and Technology
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• v.2 no.3
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• pp.353-357
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• 2007

The bearing diagnostics method is presented in this paper using fuzzy inference based on vibration data. Both time-domain and frequency-domain features are used as input data for bearing fault detection. The Adaptive Network based Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA) have been proposed to select the fuzzy model input and output parameters. Training results give the optimized fuzzy inference system for bearing diagnosis based on measured vibration data. The result is also tested with other sets of bearing data to illustrate the reliability of the chosen model.

• Convergence Security Journal
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• v.9 no.2
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• pp.1-6
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• 2009

Most diagnosis algorithms have been done using the characteristic of t-diagnosable system based on PMC model. But as parallel systems grow fast, more faulty units occur in the system. Previous researches are done on the assumption of small number of faulty units in the system. There have been little studies on the system where number of faulty units exceed t. In this study, we assume the number of faulty units exceed t and there exist small number of nodes where the correctness of diagnosis can't be decided, then we propose an algorithm which increase the maximum number of faulty units in diagnosis system.

• The Journal of the Acoustical Society of Korea
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• v.7 no.4
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• pp.77-85
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• 1988

Many kinds of conditioning monitoring technique have been studied, so this study has inverstigated the possibility of checking the trend in the fault diagnosis of ball bearing, one of the elements of rotating machine, by applying the cepstral analyisis method using the adaptive noise cancelling (ANC) method. And computer simulation is conducted in order to verify the usefulness of ANC. The optimal adaptation gain in adaptive filter is estimated, the performance of ANC according to the change of the signal to noise ratio and convergence of least mean square algorithm is considered by simulation. It is verified that cepstral analysis using ANC method is more effective than the conventional cepstral analysis method in bearing fault diagnosis.

• Journal of Information Processing Systems
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• v.16 no.3
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• pp.530-540
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• 2020

In order to solve the problem of low tracking accuracy caused by complex noise in the fault diagnosis of complex nonlinear system, a fault diagnosis method of high precision cost reference particle filter (CRPF) is proposed. By optimizing the low confidence particles to replace the resampling process, this paper improved the problem of sample impoverishment caused by the sample updating based on risk and cost of CRPF algorithm. This paper attempts to improve the accuracy of state estimation from the essential level of obtaining samples. Then, we study the correlation between the current observation value and the prior state. By adjusting the density variance of state transitions adaptively, the adaptive ability of the algorithm to the complex noises can be enhanced, which is expected to improve the accuracy of fault state tracking. Through the simulation analysis of a fuel unit fault diagnosis, the results show that the accuracy of the algorithm has been improved obviously under the background of complex noise.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.827-838
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• 2023

Centrifugal pump is a key part of nuclear power plant systems, and its health status is critical to the safety and reliability of nuclear power plants. Therefore, fault diagnosis is required for centrifugal pump. Traditional fault diagnosis methods have difficulty extracting fault features from nonlinear and non-stationary signals, resulting in low diagnostic accuracy. In this paper, a new fault diagnosis method is proposed based on the improved particle swarm optimization (IPSO) algorithm-based variational modal decomposition (VMD) and relevance vector machine (RVM). Firstly, a simulation test bench for rotor faults is built, in which vibration displacement signals of the rotor are also collected by eddy current sensors. Then, the improved particle swarm algorithm is used to optimize the VMD to achieve adaptive decomposition of vibration displacement signals. Meanwhile, a screening criterion based on the minimum Kullback-Leibler (K-L) divergence value is established to extract the primary intrinsic modal function (IMF) component. Eventually, the factors are obtained from the primary IMF component to form a fault feature vector, and fault patterns are recognized using the RVM model. The results show that the extraction of the fault information and fault diagnosis classification have been improved, and the average accuracy could reach 97.87%.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.6
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• pp.70-77
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• 2004

In many industrial applications, an unexpected fault of induction motor drive systems can cause serious troubles such as downtime of the overall system heavy loss, and etc. As one of methods to solve such problems, this paper investigates the fault diagnosis for open-switch damages in a voltage-fed PWM inverter for induction motor drive. For the feature extraction of a fault we transform the current signals to the d-q axis and calculate mean current vectors. And then, for diagnosis of different fault patterns, we propose a clustering based diagnosis algorithm The proposed diagnostic technique is a modified ANFIS(Adaptive Neuro-Fuzzy Inference System) which uses a clustering method on the premise of general ANFIS's. Therefore, it has a small calculation and good performance. Finally, we implement the method for the diagnosis module of the inverter with MATLAB and show its usefulness.