Classifying Tunnel Anomalies Based on Ultraweak FBGs Signal and Transductive RVM Combined With Gaussian Mixture Model

For long-distance unattended metro tunnel monitoring systems, accurate identification of abnormal disturbances is of great significance for underground safety. Many abnormal disturbance data will be generated in the ultraweak grating sensing system along the metro tunnel. The classification and labeling of these abnormal disturbance signals is a very large and time-consuming task. Therefore, we propose an active learning method using a transductive relevance vector machine based on a Gaussian mixture model (GMM-TRVM) to classify and identify those signals. First, we analyzed the dynamic response signals of fiber Bragg grating sensors in the metro tunnel. Then, we extracted the time-domain and frequency-domain features of the abnormal disturbance signal. Taking the characteristics of abnormal disturbance signals as input and corresponding disturbance categories as output, the training of the GMM-TRVM model was completed under the active learning framework. In the circular query process, the model was evaluated after each update, and the evaluation index was assigned an ${F}_{{1}}$ score. The final results show that the proposed GMM-TRVM active learning method achieves good classification and recognition.