Event identification of a phase-sensitive OTDR sensing system based on principal component analysis and probabilistic neural network*

To reduce the nuisance alarm rate (NAR) in phase-sensitive OTDR sensing system, a novel event identification model based on principal component analysis (PCA) and probabilistic neural network (PNN) is proposed. By training a PCA-PNN model, five kinds of disturbance events including four kinds of real disturbance and one kind of false disturbance can be effectively identified. Experimental results indicate that the average identification rate of five kinds of events reach 97.74%, with an average response time of 0.93 s. Multiple events identification with a high identification rate and fast response makes the proposed method more adaptable in practical application.

Automated summary

In order to reduce the nuisance alarm rate in phase-sensitive OTDR sensing system, a novel event identification model based on PCA and PNN was proposed. By training this model, five kinds of disturbance events can be effectively identified with an average identification rate of 97.74% and an average response time of 0.93 s. The high identification rate and fast response time make this method more adaptable in practical application.