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Unsupervised learning method for events identification in φ-OTDR
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DOI：10.1007/s11082-022-03748-y.png)
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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.
Keyword:
Fiber-optic distributed vibration sensor
Phase-sensitive optical time domain reflectometry (phi-OTDR)
Unsupervised learning
Clustering
Event identification
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4
论文数:
9.9K
被引数:
1.8W
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