Unsupervised learning method for events identification in φ-OTDR

摘要

In this paper, an unsupervised-learning method for events-identification in phi-OTDR fiber-optic distributed vibration sensor is proposed. The different vibration-events including blowing, raining, direct and indirect hitting, and noise-induced false vibration are clustered by the k-means algorithm. The equivalent classification accuracy of 99.4% has been obtained, compared with the actual classes of vibration-events in the experiment. With the cluster-number of 3, the maximal Calinski-Harabaz index and Silhouette coefficient are obtained as 2653 and 0.7206, respectively. It is found that our clustering method is effective for the events-identification of phi-OTDR without any prior labels, which provides an interesting application of unsupervised-learning in self-classification of vibration-events for phi-OTDR.

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