# A Fiber Vibration Signal Recognition Method Based on CNN-CBAM-LSTM
## 6 Conclusions  
In traditional pattern recognition algorithms, most methods involve modal decomposition of signals to obtain features, but these methods are prone to modal aliasing and rely on manual parameter design, which has certain limitations. Aimed at this limitation, a deep learning method of CNN-CBAM-LSTM is proposed to extract features. The specific work of this paper is as follows:  
1. In terms of data processing, the collected OFVS are denoised using spectral subtraction, and the endpoint detection is then carried out by fusing spectral entropy and short-term energy to determine the signal's starting position. The isometric signal segment with 80 K sampling points is intercepted in accordance with the starting point, and then the signal is normalized. 2. In order to fully utilize the vibrational data in the original signal, the equal-length signal fragments are completely input into a \(1*1\) convolutional layer. On the one hand, 80 different convolution kernels (equivalent to filters) are used for filtering operations to filter different frequency bands of the signal; On the other hand, multiple convolution kernels can generate multiple different feature maps and enhance the information integration between different channels. It provides a better basis for the subsequent use of general CNNs to extract signal features. 3. In order to process the feature information finely, this paper introduces the CBAM attention module, which can create attention feature maps in both space and channels, multiply them with the original feature map, and perform adaptive feature rectification. The mechanism of channel attention is employed to select feature maps that have an important impact on pattern recognition from an abundance of feature maps, simultaneously; the spatial attention is employed to select effective feature information from the spatial information of the feature maps. When the two are combined, significant feature information might be given a larger feature weight. 4. The feature information obtained after the previous step is input into the LSTM module, the feature time series information of the OFVS is extracted, and it will automatically learn the important features that are beneficial to the vibration signal pattern recognition. Finally, this is combined with MLP to complete the classification task of OFVS. Through the experiment, it was discovered that when compared to the conventional method VMD, the CNN-CBAM-LSTM model significantly improved the recognition effect of various types of OFVSs. In addition, the model parameters were automatically updated through network iteration, which can adapt to different environments and has good generalization ability. Compared with the AF-CNN and CNN-LSTM, the model put forth in this paper can clearly obtain higher average recognition accuracy. It may also be observed from the accuracy curve and loss curve that the model has a better fitting effect. In \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**RecognitionMethod** & **VMD** & **AF-CNN** & **CNN-LSTM** & **CNN-CBAM-LSTM** \\ \hline Flap & 93.3\% & 94.6\% & 97.5\% & 96.4\% \\ knock & 99.3\% & 93.4\% & 92.4\% & 99.9\% \\ Walk & 86.9\% & 100\% & 100\% & 99.9\% \\ Run & 92.9\% & 98.5\% & 98.4\% & 95.5\% \\ Average & 92.2\% & 96.7\% & 97.1\% & 97.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of different models. conclusion, the CNN-CBAM-LSTM method is feasible and effective for the identification of OFVS. In the future, with the continuous development of artificial intelligence algorithms, we can continue to study more excellent OFVS recognition methods from the aspects of endpoint detection and recognition algorithms. Conceptualization, [PERSON]; Data curation, [PERSON]; Investigation, [PERSON] and [PERSON]; Methodology, [PERSON]; Software, [PERSON]; Validation, [PERSON]; Visualization, [PERSON]; Writing--original draft, [PERSON]; Writing--review and editing, [PERSON] All authors have read and agreed to the published version of the manuscript.