Distributed Fiber Optic Vibration Sensing Event Recognition Method Based on CNN-LSTM-Transformer Net

Phase-sensitive Optical Time-Domain Reflectometer (φ-OTDR) technology facilitates the real-time detection of vibration events along fiber optic cables by analyzing changes in Rayleigh scattering signals. This technology is widely used in applications such as intrusion monitoring and structural health assessments. Traditional signal processing methods, such as Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), have limitations in feature extraction and classification in complex environments. Conversely, a single deep learning model often struggles with capturing long time-series dependencies and mitigating noise interference. In this study, we propose a deep learning model that integrates Convolutional Neural Network (CNN), Long Short-Term Memory Network (LSTM), and Transformer modules, leveraging φ-OTDR technology for distributed fiber vibration sensing event recognition. The hybrid model combines the CNN's capability to extract local features, the LSTM's ability to model temporal dynamics, and the Transformer's proficiency in capturing global dependencies. This integration significantly enhances the accuracy and robustness of event recognition. In experiments involving six types of vibration events, the model consistently achieved a validation accuracy of 0.92, and maintained a validation loss of approximately 0.2, surpassing other models, such as TAM+BiLSTM and CNN+CBAM. The results indicate that the CNN+LSTM+Transformer model is highly effective in handling vibration signal classification tasks in complex scenarios, offering a promising new direction for the application of fiber optic vibration sensing technology.