• Nuclear Engineering and Technology
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• 제54권4호
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• pp.1230-1244
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• 2022

This paper proposes an algorithm for signal validation using unsupervised methods in emergency situations at nuclear power plants (NPPs) when signals are rapidly changing. The algorithm aims to determine the stuck failures of signals in real time based on a variational auto-encoder (VAE), which employs unsupervised learning, and long short-term memory (LSTM). The application of unsupervised learning enables the algorithm to detect a wide range of stuck failures, even those that are not trained. First, this paper discusses the potential failure modes of signals in NPPs and reviews previous studies conducted on signal validation. Then, an algorithm for detecting signal failures is proposed by applying LSTM and VAE. To overcome the typical problems of unsupervised learning processes, such as trainability and performance issues, several optimizations are carried out to select the inputs, determine the hyper-parameters of the network, and establish the thresholds to identify signal failures. Finally, the proposed algorithm is validated and demonstrated using a compact nuclear simulator.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2008년도 추계학술대회A
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• pp.938-942
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• 2008

To detect the failures in machine, the signals of current, vibration and acoustic emission are widely used in industry. And unexpected failures of gears are not only extremely damaging but also lead to economic losses. In this paper, to detect the misalignment occurred at between two gears in gearboxes, the signals of current, vibration and AE were measured at gearbox and motor power line. FFT(Fast Fourier Transform) was used for current and vibration signal analysis to find gear failure frequency. Especially, the envelop analysis and wavelet transform were applied for AE signal. Therefore, compared with the results of three kinds of signal, the possibility of earily detection by AE is identified or checked.

• Journal of Electrical Engineering and Technology
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• 제8권2호
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• pp.288-294
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• 2013

The diagnosis of motor failures using an on-line method has been the aim of many researchers and studies. Several spectral analysis techniques have been developed and are used to facilitate on-line diagnosis methods in industry. This paper discusses the first application of a motor flux spectral analysis to the identification of broken rotor bar (BRB) faults in induction motors using a multiple signal classification (MUSIC) technique as an on-line diagnosis method. The proposed method measures the leakage flux in the radial direction using a radial flux sensor which is designed as a search coil and is installed between stator slots. The MUSIC technique, which requires fewer number of data samples and has a higher detection accuracy than the traditional fast Fourier transform (FFT) method, then calculates the motor load condition and extracts any abnormal signals related to motor failures in order to identify BRB faults. Experimental results clearly demonstrate that the proposed method is a promising candidate for an on-line diagnosis method to detect motor failures.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2002년도 추계학술대회논문집
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• pp.617-622
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• 2002

This paper presents that the Wavelet Transform can be used to detect the various local defects in a gearbos. Two types of defects which are broken tooth and localized wear, are experimented and the signals are collected by accerometer and analyzed. Because of the complecity of the signals acquired from sensor, it is needed to identify the interesting signal. The natural frequencies of shafts and the gear mesh frequency(GMF) is calculated theretically. DWT, CWT and the aplication are used to extract a gear-localized defect feature from the vibration signal of the gearbox with the defective gear. The results shows the transform is more effective to detect the failures than the Fourier Transform.

• 한국항행학회논문지
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• 제7권2호
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• pp.171-179
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• 2003

본 논문에서는 항공기 센서와 구동장치 고장시 신경회로망을 이용하여 이를 대처하는 고장보완시스템에 대해서 고려한다. 센서 고장의 경우에는 비행동역학적 관계식을 적절히 이용하여 신경회로망으로 센서고장을 진단/규명하고 고장난 센서를 대체할 수 있는 시스템을 설계하고 또한 구동장치의 고장이나 조종면의 일부 파손시 이를 진단/규명하고 보완하여 새로운 트림상태로 안정화시키는 제어시스템을 제안한다. 설계된 두 보완시스템을 하나의 가격함수로 통합하여 운용하는 알고리즘을 제안하며 이의 검증을 위해 센서와 구동장치의 고장을 가상적으로 설정하여 시뮬레이션함으로써 보완시스템의 성능을 확인한다.

• International Journal of Precision Engineering and Manufacturing
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• 제5권2호
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• pp.46-52
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• 2004

Numerous connectors are used in automobiles for transmission of electrical signals across various electro-mechanical components. The connectors must operate with high reliability in order to minimize failures due to signal degradation. In this work, the effects of contamination at the contact interface of connectors used fur automobile crankshaft position sensor on the impedance change were investigated. An experimental set-up was built to simulate the electrical signal transmitted from the sensor to the engine control unit through a connector. Output from the connector was investigated using connectors contaminated with engine block residues and water droplets. It was found that slight contamination of the connectors could lead to significant signal degradation which can lead to engine failure. Also, the effect of water in the connector altered the signal severely. However, the signal gradually regained the original state as the water evaporated from the interface.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 추계학술대회
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• pp.585-590
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• 2004

Many machine failures are not detected well in advance due to the masking of background noise and attenuation of the source signal through the transmission mediums. Advanced signal processing techniques using adaptive filters and higher order statistics have been attempted to extract the source signal from the measured data at the machine surface. In this paper, blind deconvolution using the eigenvector algorithm (EVA) technique is used to recover a damaged bearing signal using only the measured signal at the machine surface. A damaged bearing signal corrupted by noise with varying signal-to-noise (s/n) was used to determine the effectiveness of the technique in detecting an incipient signal and the optimum choice of filter length. The results show that the technique is effective in detecting the source signal with an s/n ratio as low as 0.21, but requires a relatively large filter length.

• 산업경영시스템학회지
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• 제46권2호
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• pp.168-175
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• 2023

In the era of the 4th Industrial Revolution, Logistic 4.0 using data-based technologies such as IoT, Bigdata, and AI is a keystone to logistics intelligence. In particular, the AI technology such as prognostics and health management for the maintenance of logistics facilities is being in the spotlight. In order to ensure the reliability of the facilities, Time-Based Maintenance (TBM) can be performed in every certain period of time, but this causes excessive maintenance costs and has limitations in preventing sudden failures and accidents. On the other hand, the predictive maintenance using AI fault diagnosis model can do not only overcome the limitation of TBM by automatically detecting abnormalities in logistics facilities, but also offer more advantages by predicting future failures and allowing proactive measures to ensure stable and reliable system management. In order to train and predict with AI machine learning model, data needs to be collected, processed, and analyzed. In this study, we have develop a system that utilizes an AI detection model that can detect abnormalities of logistics rotational equipment and diagnose their fault types. In the discussion, we will explain the entire experimental processes : experimental design, data collection procedure, signal processing methods, feature analysis methods, and the model development.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2008년도 추계학술대회논문집
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• pp.43-48
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• 2008

Gear system has been widely used in industrial applications and unexpected failures of gears are not only extremely damaging but also lead to economic losses. So, early detection of fault is important for diagnosis machine condition. And acoustic emission is an efficient non destructive testing technique for the diagnosis of machine health and is useful technique for early detection of fault because it can find low-amplitude and high-frequency signal on account of high sensibility. Therefore, in this paper, the AE signal was measured and preprocessed using envelop analysis for gearbox with misalignment between pinion and gear. And then the vibration characteristic of gear misalignment was analyzed.

• 한국소음진동공학회논문집
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• 제18권12호
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• pp.1243-1249
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• 2008

Gear system has been widely used in industrial applications and unexpected failures of gears are not only extremely damaging but also leading to economic losses. So, early detection of fault is important for diagnosis machine condition. And acoustic emission is an efficient non-destructive testing technique fur the diagnosis of machine health and is useful technique far early detection of fault because it can find low-amplitude and high-frequency signal on account of high sensibility. Therefore, in this paper, the AE signal was measured and preprocessed using envelope analysis for gearbox with misalignment between pinion and gear. And then the gear misalignment's vibration characteristic were analyzed.