• Journal of Information Processing Systems
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• v.18 no.6
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• pp.822-829
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• 2022

According to existing study into the remote fault diagnosis procedure, the current diagnostic approach has an imperfect decision model, which only supports communication in a close distance. An Internet of Things (IoT)-based remote fault diagnostic approach for wind power equipment is created to address this issue and expand the communication distance of fault diagnosis. Specifically, a decision model for active power coordination is built with the mechanical energy storage of power generation equipment with a remote diagnosis mode set by decision tree algorithms. These models help calculate the failure frequency of bearings in power generation equipment, summarize the characteristics of failure types and detect the operation status of wind power equipment through IoT. In addition, they can also generate the point inspection data and evaluate the equipment status. The findings demonstrate that the average communication distances of the designed remote diagnosis method and the other two remote diagnosis methods are 587.46 m, 435.61 m, and 454.32 m, respectively, indicating its application value.

• Journal of Power Electronics
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• v.9 no.2
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• pp.224-231
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• 2009

The induction motor rotor fault diagnosis system using current signals, which are measured using an axis-transformation method, is presented in this paper. In inverter-fed motor drives, unlike line-driven motor drives, the stator currents are rich in harmonics; therefore fault diagnosis using stator current is not trivial. The current signals for rotor fault diagnosis need precise and high resolution information, which means the diagnosis system demands additional hardware such as a low pass filter, high resolution ADC, and encoder, etc. The proposed axis-transformation method with encoder and without encoder is expected to contribute to a low cost fault diagnosis system in inverter-fed motor drives without the need for any additional hardware. In order to confirm the validity of the developed algorithms, various experiments for rotor faults are tested and the line current spectrum of each faulty situation using Park transformation is compared with the results obtained from fast Fourier transforms.

• Journal of Power Electronics
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• v.18 no.5
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• pp.1608-1617
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• 2018

The rapid and correct isolation of faulty submodules (SMs) is of great importance for improving the reliability of modular multilevel converters (MMCs). Therefore, a fast diagnosis method containing fault detection and fault location determination was presented in this paper. An improved incremental predictive model of arm current was proposed to detect failures, and the multi-step prediction method was used to eliminate the negative impact of disturbances. Moreover, a control method was proposed to strengthen the fault characteristics to rapidly locate faulty arms and faulty SMs by detecting the variation rate of the SM capacitor voltage. The proposed method can rapidly and easily locate faulty SMs under different load conditions without the need for additional sensors. The experimental results have validated the effectiveness of the proposed method by using a single-phase MMC with four SMs per arm.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.301-305
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• 2001

A novel approach based on parameters estimation is presented far fault detection and diagnosis in sensors. Based on known precise parameter of normal working sensors system model is built from real laboratory inputs-outputs data, sequentially residual serial is obtained. Where decision-making rule of detection the fault is given via the use of beys theory, whilst a filter least-square computative algorithm for estimating fault parameters is given. The algorithm is a fast and accurate to calculate value of sensors faults when system model contains noise and sensors outputs contain measured noise. The method can solve both gain type and bias type fault in sensors. Simulated numerical example is included to demonstrate the use of the proposed approaches.

• Journal of Advanced Marine Engineering and Technology
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• v.31 no.1
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• pp.95-102
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• 2007

The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis and prognosis are of increasing importance. This study introduces a technique to detect and identify faults in induction motors. Stator currents were measured and stored by time domain. The time domain is not suitable for representing current signals, so wavelet transform is used to convert the signals onto frequency domain. The raw signals can not show the significant feature, therefore difference values between the signal of the health conditions and that of the fault conditions are applied. The difference values were transformed by wavelet transform and the features are extracted from the transformed signals. The dynamic time warping method was used to identify the fault type. This study describes the results of detecting fault using wavelet analysis.

• IE interfaces
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• v.25 no.2
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• pp.170-177
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• 2012

Process control is essential to operate the semiconductor process efficiently. This paper consider fault classification of semiconductor based cyclic signal for process control. In general, process signal usually take the different pattern depending on some different cause of fault. If faults can be classified by cause of faults, it could improve the process control through a definite and rapid diagnosis. One of the most important thing is a finding definite diagnosis in fault classification, even-though it is classified several times. This paper proposes the method that one-class classifier classify fault causes as each classes. Hotelling T2 chart, kNNDD(k-Nearest Neighbor Data Description), Distance based Novelty Detection are used to perform the one-class classifier. PCA(Principal Component Analysis) is also used to reduce the data dimension because the length of process signal is too long generally. In experiment, it generates the data based real signal patterns from semiconductor process. The objective of this experiment is to compare between the proposed method and SVM(Support Vector Machine). Most of the experiments' results show that proposed method using Distance based Novelty Detection has a good performance in classification and diagnosis problems.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.1
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• pp.24-32
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• 2002

This paper is concerned with the fault diagnosis for stuck-at faults of a multistage interconnection network(MIM) which is a kind of interconnection networks in multicomputer systems. Up to the present, a fault diagnosis scheme has dealt with a fault model of all types, which results in complicated algorithms. In the literature, it is shown that a number of steps and computation are required for the fault detection and isolation algorithms for a class of MINs. In this paper, we propose a simple and easily implementable algorithm for the detection and isolation of the stuck-at fault in MIM. specifically, we develope an at algorithm for the isolation of the source fault in switching elements whenever tile stuck-at fault is detected in MINs. After all, the proposed algorithm is illustrated by an example of 16$\times$16 baseline networks of MINs.

• Journal of the Korean Society for Nondestructive Testing
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• v.31 no.2
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• pp.134-138
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• 2011

Fault detection for dynamic loading conditions of rotational machineries was considered from the contactless, non-destructive infrared thermographic method, rather than the traditional diagnosis method. In this paper, by applying a rotating deep-grooved ball bearing, passive thermographic experiment was performed as an alternative way proceeding the traditional fault monitoring. In addition, the thermographic experiments were compared with the vibration spectrum analysis to evaluate the efficiency of the proposed method. Based on the results, it was concluded the temperature characteristics of the ball bearing under dynamic loading conditions were analyzed thoroughly.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.674-679
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• 1987

Process failures can occur at any time during operation, so a continuous effort of fault detection, diagsis, and correction is required. Expert system paridigm has been regarded as a promising approach to real time process supervisory control especially to fault diagnosis. The most important aspects of fault diagnostic expert systems(FDES) are the problem-solving inference strategy and knowledge organizations. The necessity of FDES, the nature of diagnostic knowledge, the representation of knowledge, and the inference mechanism of FDES, et al. are described, which are announced by previous researchers. And the existing FDES are categorized and critically reviewed in this work.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.33.3-33
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• 2001

This paper considers a fault detection and diagnosis using estimation method in uncertain systems. In the state estimation method, we use the robust H$\infty$ FIR filtering algorithm. A novel aspect of the fault detection technique described here is that it explicitly accounts for the effects of simplified models and errors due to the linearization of nonlinear systems at an operating point.