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A platform for research: civil engineering, architecture and urbanism
A Bayesian Adaptive Resize-Residual Deep Learning Network for Fault Diagnosis of Rotating Machinery
Due to the high accuracy achieved in data-driven fault diagnosis, time-frequency images generated by Continuous Wavelet Transform (CWT) are widely used as the input of deep learning methods. However, the image data require huge amount of data memories. An adaptive resize technique provides a reliable way for deducing the scale of image and achieving good effect. In this study, a novel Bayesian adaptive resize-residual network was proposed to resize the input data scale and extract the image feature for mechanical fault diagnosis. The CWT and Histogram Equalization (HE) algorithm were used to generate enhanced time-frequency images. The newly developed adaptive resize-residual network was applied for feature extraction, in which the adaptive resize block can adaptively resize input image by self-learning, and the residual block was used for classification. The Bayesian optimization was introduced to optimize the model hyper parameters and obtain an effective model. A testbeds of rolling element bearings are introduced to support the experiments. The experimental results indicate that the proposed Bayesian adaptive resize-residual network obtains superior recognition accuracy and outperforms many state-of-the-art methods. This method is conducive to improving the capabilities of rotating machinery fault diagnosis, and reduces the repair time of fault.
A Bayesian Adaptive Resize-Residual Deep Learning Network for Fault Diagnosis of Rotating Machinery
Due to the high accuracy achieved in data-driven fault diagnosis, time-frequency images generated by Continuous Wavelet Transform (CWT) are widely used as the input of deep learning methods. However, the image data require huge amount of data memories. An adaptive resize technique provides a reliable way for deducing the scale of image and achieving good effect. In this study, a novel Bayesian adaptive resize-residual network was proposed to resize the input data scale and extract the image feature for mechanical fault diagnosis. The CWT and Histogram Equalization (HE) algorithm were used to generate enhanced time-frequency images. The newly developed adaptive resize-residual network was applied for feature extraction, in which the adaptive resize block can adaptively resize input image by self-learning, and the residual block was used for classification. The Bayesian optimization was introduced to optimize the model hyper parameters and obtain an effective model. A testbeds of rolling element bearings are introduced to support the experiments. The experimental results indicate that the proposed Bayesian adaptive resize-residual network obtains superior recognition accuracy and outperforms many state-of-the-art methods. This method is conducive to improving the capabilities of rotating machinery fault diagnosis, and reduces the repair time of fault.
A Bayesian Adaptive Resize-Residual Deep Learning Network for Fault Diagnosis of Rotating Machinery
Lecture Notes in Civil Engineering
Geng, Guoqing (editor) / Qian, Xudong (editor) / Poh, Leong Hien (editor) / Pang, Sze Dai (editor) / Zou, L. (author) / Zhuang, K. J. (author) / Hu, J. (author)
2023-03-14
19 pages
Article/Chapter (Book)
Electronic Resource
English
Bayesian optimization , Deep learning , Continuous wavelet transform (CWT) , Adaptive resize-residual network , Fault diagnosis Engineering , Building Construction and Design , Structural Materials , Solid Mechanics , Sustainable Architecture/Green Buildings , Light Construction, Steel Construction, Timber Construction , Offshore Engineering
| Online Contents | 2005
Data Mining for Fault Diagnosis and Machine Learning for Rotating Machinery
| British Library Online Contents | 2005
ROTATING MACHINERY FAULT DIAGNOSIS BASED ON TWO-DIMENSIONAL CONVOLUTION NEURAL NETWORK
| DOAJ | 2020