Unsupervised learning method for events identification in φ -OTDR
Optical and Quantum Electronics(2022)
摘要
In this paper, an unsupervised-learning method for events-identification in φ -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 φ -OTDR without any prior labels, which provides an interesting application of unsupervised-learning in self-classification of vibration-events for φ -OTDR.
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关键词
Fiber-optic distributed vibration sensor, Phase-sensitive optical time domain reflectometry (φ-OTDR), Unsupervised learning, Clustering, Event identification
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