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RISS 인기검색어
- 검색키워드
- 저자 : Osama Al-Azzawi (검색결과 2 건)
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Osama Al-Azzawi,Dansheng Wang 대한기계학회 2021 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.35 No.5
Recently, Intelligence-based structural health monitoring (SHM) methods have investigated widely. Most of these methods are for detecting and classifying different structural damages by the means of features extraction from the structural responses signals, for instance different back propagation artificial neural networks SHM based methods. However, automatic features extraction, that eliminates the need for expertise and performing visual inspection to evaluate structures status is still a big challenge. In this study, therefore, a novel convolution neural network-based algorithm along with a hybrid training method has been proposed to detect, quantify and localize structural damage. The proposed method has been evaluated experimentally, many damaged and undamaged structural conditions have been conducted, acquiring samples of time-domain PZT impedance response signals from a beam. As the results show that, the method obtained a significant execution on damage detection, damage size evaluation and damage location recognition with high accuracy and reliability.
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Anas H. Aljemely,Jianping Xuan,Farqad K. J. Jawad,Osama Al-Azzawi,Ali S. Alhumaima 대한기계학회 2020 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.34 No.11
Recently, several studies tried to develop fault identification models for rolling element bearing based on unsupervised learning techniques. However, an accurate intelligent fault diagnosis system is still a big challenge. In this study, a deep functional auto-encoders (DFAEs) model with SoftMax classifier was designed for valuable feature extraction from massive raw vibration signals. To maximize the unsupervised feature learning ability of the proposed model, various activation functions were applied in an effective methodology, these hidden activation functions enhance significantly the sparsity of the training data-set. The proposed method was validated using the raw vibration signals measured from the machine with different bearing conditions. The achieved results showed that the high-superiority of the proposed model comparing to standard deep learning and other traditional fault diagnosis methods in terms of classification accuracy even with massive input data sets.
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