Abstract:
A new CNN-based deep neural network, multi-scale one-dimensional convolutional neural network (MS 1-D CNN) was proposed to improve the efficiency and accuracy of vibration event recognition for a phase-sensitive optical time-domain reflectometry (Φ-OTDR) distributed optical fiber vibration sensing system. The raw vibration signals are pre-processed first to remove noise as far as possible. The pre-processing operations include pre-emphasis filtering, normalization and spectral subtraction. The pre-processed signals are used as the inputs of MS 1-D CNN directly. MS 1-D CNN realizes the end-to-end feature extraction of vibration signals and finally recognizes the vibration events by using a fully-connected layer (FC layer) and a Softmax layer. In comparison with two-dimensional convolutional neural network (2-D CNN) and one-dimensional convolutional neural network (1-D CNN), the proposed method balances the time and frequency scales better during feature extraction and reduces the pending parameters of the whole neural network. A vibration recognition experiment was designed to classify the three types of the vibration events including damaging, knocking and interference. The recognition results show that MS 1-D CNN achieves similar recognition performance, over 96 percent, at twice processing speed compared to 2-D CNN. Therefore, it is beneficial to improve the real-timing of vibration monitoring while maintaining the recognition performance.
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Abstract: A new CNN-based deep neural network, multi-scale one-dimensional convolutional neural network (MS 1-D CNN) was proposed to improve the efficiency and accuracy of vibration event recognition for a phase-sensitive optical time-domain reflectometry (Φ-OTDR) distributed optical fiber vibration sensing system. The raw vibration signals are pre-processed first to remove noise as far as possible. The pre-processing operations include pre-emphasis filtering, normalization and spectral subtraction. The pre-processed signals are used as the inputs of MS 1-D CNN directly. MS 1-D CNN realizes the end-to-end feature extraction of vibration signals and finally recognizes the vibration events by using a fully-connected layer (FC layer) and a Softmax layer. In comparison with two-dimensional convolutional neural network (2-D CNN) and one-dimensional convolutional neural network (1-D CNN), the proposed method balances the time and frequency scales better during feature extraction and reduces the pending parameters of the whole neural network. A vibration recognition experiment was designed to classify the three types of the vibration events including damaging, knocking and interference. The recognition results show that MS 1-D CNN achieves similar recognition performance, over 96 percent, at twice processing speed compared to 2-D CNN. Therefore, it is beneficial to improve the real-timing of vibration monitoring while maintaining the recognition performance.
