Recognition algorithm for optic fiber vibration signals based on improved CLDNN

Current optical fiber vibration signal (OFVS) recognition algorithms are limited by insufficient convolutional layer receptive fields, typically relying on a single convolutional layer for feature extraction. This approach often overlooks critical long-range dependencies between channels and fails to capture fine-grained features, leading to difficulties in distinguishing fiber optic vibration signals with similar energy levels and waveforms. To address these shortcomings, this paper proposes the FDSF-CLDNN model (Fusing Dependencies and Small Local Features), building on the CLDNN framework. The GCSconv module is introduced to emphasize long-range dependencies and local fine features, thereby enhancing the extraction of both global channel and spatial features, allowing the model to focus more effectively on key features and spatial locations. Additionally, to preserve fine-grained features while maintaining long-range channel dependencies, this paper proposes the EVC-SCconv module. This module integrates a self-calibration mechanism to amplify inter-channel information, extend the convolutional kernel's receptive field, and accentuate the salient characteristics of the vibration signals. Simultaneously, it leverages an attention mechanism to retain global feature dependencies while highlighting local small features. Furthermore, to maintain time-domain signal dependencies while reducing computational complexity and improving model efficiency, the LSTM module in the original architecture is replaced by the GRU module, which achieves computational savings without sacrificing recognition accuracy. Experimental results demonstrate that the proposed FDSF-CLDNN model achieves a recognition accuracy of 99.0% for optical fiber vibration signals, outperforming other models in terms of both accuracy and stability. Moreover, the proposed model converges more rapidly and yields higher average recognition accuracy compared to existing approaches.