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Dual-FBG bearing fault probe based on a CNN-LSTM-encoder network

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Abstract

A centimeter-sized bearing fault probe based on dual-fiber Bragg grating vibration sensing is proposed. The probe can provide multi-carrier heterodyne vibration measurements based on swept source optical coherence tomography technology and the synchrosqueezed wavelet transform method to obtain a wider vibration frequency response range and collect more accurate vibration data. For the sequential characteristics of bearing vibration signals, we propose a convolutional neural network with long short-term memory and transformer encoder. This method is proven in bearing fault classification under variable working conditions, and the accuracy rate reaches 99.65%.

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Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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