# A Fiber Vibration Signal Recognition Method Based on CNN-CBAM-LSTM
###### Abstract  
By trying to solve the issue of identifying multiple types of intrusion vibration signals collected by distributed vibrating fiber optic sensors, this study investigates the signal identification and feature extraction of intrusion signals, and proposes an optical fiber vibration signal (OFVS) identification method based on deep learning. The external vibration signal is collected by the Sagnac fiber optic interferometer, and then denoised by spectral subtraction. Endpoint detection is carried out by combining the short-time logarithmic energy method and the spectral entropy method. Finally, the equal-length signal containing valid information is intercepted and the corresponding preprocessing is carried out. The method for feature processing incorporates the strong feature learning capability of the long-short-term memory (LSTM) and the great short-term feature extraction capability of the convolutional neural network (CNN). At the same time, to further enhance the signal feature identification, a convolutional block attention module (CBAM) is introduced to perform adaptive feature refinement on the signal. In summary, a network model combining CNN, LSTM, and CBAM is proposed to process the signal features, and finally, the multi-layer perceptron (MLP) is used to complete the task of classification and recognition of multi-type intrusion signals. The experimental findings indicate that the OFVS method of CNN-CBAM-LSTM can effectively identify four kinds of OFVS, and the overall average recognition accuracy reaches 97.9%. Walking and knocking signals among them are recognized with over 99% accuracy. optical fiber vibration signal; convolutional neural network; long short-term memory; convolutional block attention module +
Footnote †: journal: Applied  
optimization for classification [13]. The effect of modal aliasing still exists. Later, some scholars proposed the complementary ensemble empirical mode decomposition (CEEMD) method [14], which can suppress the residual white noise in EEMD. Artificial neural networks are frequently employed in a variety of industries, including monitoring product quality in production and forecasting some key factors in the industry [15; 16; 17; 18]. In addition, the convolutional neural network (CNN) is crucial for computer vision, object detection, complicated network classification, pattern recognition, and other fields of study [19; 20; 21; 22; 23]. [PERSON] et al. combined time-frequency analysis and CNN algorithm to extract features, and used the multi-class SVM recognition algorithm to replace the softmax layer for signal recognition [24]. In the situation of a low signal-to-noise ratio, this technique can overcome the drawbacks of human feature selection. [PERSON] et al. proposed an adaptive filtering convolutional neural network (AF-CNN), which adaptively filtered the original signal using the convolution kernel, and achieved a superior recognition effect compared to the conventional approach [25]. At present, the recognition of optical fiber vibration signals still has the problem of low accuracy. In order to improve this situation and improve the stability and accuracy of fiber perimeter security systems in practical applications. This paper studies the above problems by combining this with CNN, which is widely used in various fields. CNN uses multiple convolution kernels to perform convolution processing separately, thereby extracting different types of features. When the data are one-dimensional time series data, the compact adaptive one-dimensional CNN can capture the features well, and the long short-term memory (LSTM) network has the advantage of processing data time series effectively [26]. Although CNN can autonomously learn the features of OFVS, it does not consider the temporal dependencies hidden in the signal, which is the key to forming classification features [27]. To refine the extracted features, combined with the excellent performance of the CBAM attention mechanism in many fields, a method of OFVS recognition based on CNN-CBAM-LSTM is proposed.