# A Fiber Vibration Signal Recognition Method Based on CNN-CBAM-LSTM
## 5 Experiment and Analysis  
### Signal Acquisition and Preprocessing  
The experimental data were provided by the vibration fiber sensing system independently produced by Xinjiang Meite Intelligent Safety Engineering Co., LTD., Xinjiang Uygur Autonomous Region, China. A distributed fiber optic sensing system according to a Sagnac fiber optic interferometer was used in the acquisition process, and 1212 OFVSs of the following four types were obtained: flapping (283), knocking (304), walking (300), running (325). The actual measurement environment is shown in Figure 3.  
Figure 3: The actual measurement environment: **(a)** hung nets; **(b)** buried.  
Figure 2: Channel attention and spatial attention in CBAM.  
As can be observed from the figure, the measuring environment is divided into the following two situations: hang net and buried, and they adopt an S-shaped arrangement.  
In order to make the research process more intuitive, the overall research process is shown in Figure 4.  
Among them, the first three parts are the data collection and preparation stage, and the last three parts start to study the pattern recognition of OFVS.  
In traditional pattern recognition, the signal preprocessing stage is mainly divided into two parts, noise reduction and endpoint detection. The purpose of noise reduction is to suppress the signal noise and highlight the characteristics of pure vibration signals, which is advantageous to enhance the accuracy of vibration signal detection. The noise reduction method that we chose was spectral subtraction, which can remove the noise power spectrum, and output the vibration signal's spectrum after spectral subtraction, to improve the signal-to-noise ratio of OFVS. Common endpoint detection methods include the short-time logarithmic energy method, spectral entropy method, etc. The former has improved impact on the signal with larger disturbance, and the latter can better identify the low-quality signal. Here, the fusion algorithm of short-time logarithmic energy and spectral entropy is used to detect the endpoints. The endpoints of different vibration signals can be more accurately identified by combining the benefits of the two methods.  
The process of signal preprocessing is shown in Figure 5.  
Firstly, the collected raw fiber vibration signal data are denoised by spectral subtraction, and then the endpoint detection of the OFVS is carried out by combining the short-time logarithmic energy and the spectral subtraction method. If the initial point of OFVS is detected, the data with a length of 80 K (the amount of data that can be collected within 1s of the vibration signal collecting device) sampling points will be intercepted from the starting position. It is evident from observation that an entire vibration signal can be found in the data of 80 K sample points (for walking and running signals, one of the multiple segments of similar short signals generated by them is selected as a representative). Then, one must perform the endpoint detection again to repeat the second and third steps; finally, one can normalize the intercepted data to the maximum value, and the four fiber vibration signals after preprocessing are shown in the Figure 6.  
Figure 4: Overall research flow chart.  
Figure 5: Signal preprocessing flow chart.