Event Identification by F-ELM Model for $\varphi$ -OTDR Fiber-Optic Distributed Disturbance Sensor

Abstract

A novel event identification method, which combines the extreme learning machine (ELM) and fisher score feature selection method, is proposed to reduce the nuisance alarm rate (NAR) in fiber-optic distributed disturbance sensors based on phase-sensitive optical time-domain reflectometer (φ-OTDR). Through constructing 25.05 km long φ-OTDR experimental system and analyzing the selected features with the ELM, four kinds of real disturbance events, including watering, climbing, knocking and pressing, and a false disturbance event can be effectively identified. Experimental results show that the average identification rate of five disturbance events exceeds 95%, the identification time is below 0.1 s and the NAR is 4.67% through selecting 25 features. Compared with the ELM model without feature selection, the ELM model with feature selection by fisher method has several distinguished advantages of higher identification rate, shorter identification time, and lower NAR.