Ø-GLMAE: Global-local Masked Autoencoder for Ø-OTDR Event Classification

Abstract Optical Time Domain Reflectometry (OTDR) is used extensively in distributed sensing. The Φ-OTDR, enhances diagnostic capabilities by providing detailed event classification essential for fiber infrastructure management, making it a critical tool in measurement science for real-time monitoring and fault detection in large-scale industrial and environmental systems. Recent deep learning techniques, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, have shown promise for this task, though current supervised methods struggle to utilize vast unlabeled data and fail to effectively represent Φ-OTDR signals. To address these issues, we introduce the Φ-Masked Autoencoder (Φ-MAE) framework, which leverages masked autoencoder architecture in two stages: a pre-training phase using unlabeled data to extract robust representations and a fine-tuning phase for classification refinement with labeled data. Extending this, we propose the Φ Global-local Masked Autoencoder (Φ-GLMAE) framework, which integrates local and global patterns for improved feature extraction. Experimental evaluations on the BJTU-OTDR dataset demonstrate that our proposed Φ-GLMAE achieves an accuracy of 99.74%, outperforming state-of-the-art approaches.In addition, under the 2% labeled data condition, the proposed method with pre-training achieves 87.36% accuracy, significantly outperforming the method without pre-training (64.90%) by 22.46%. The source code will be made available at https://github.com/Kevin2087528605/global_local_mae