Research and Software Design of an xcexa6OTDRBased Optical Fiber Vibration Recognition Algorithm Distributed optical fiber vibration signal plays a significant role in the communication and safety of any perimeter security monitoring system. It uses light as an information carrier and optical fiber as a means of signal transmission and communication. Phasesensitive optical timedomain reflectometry (xcexa6OTDR) is used to detect the signals generated during events (intrusions or nonintrusion). This paper proposes the timefrequency characteristic (TFC) method for the recognition of the fiber vibration signal and designs and implements the corresponding software function module. The combination of timedomain features and timefrequencydomain features is called TFC; and it is based on the Hilbert transform and on the empirical mode decomposition (EMD) of timefrequency entropy and centerofgravity frequency that is described. A feature vector is formed, and multiple types of probabilistic neural networks (PNNs) are performed on it to determine whether intrusion events occur. The experimental simulation results show that the monitoring system software can intelligently display the data collected in real time, which demonstrates that the proposed method is effective and reliable in identifying and classifying accurately the types of events. The data processing time is less than 2xe2x80x89s, and the accuracy of the system identification can reach 99%, which ensures the systemxe2x80x99s validity.

3D Printing TechniqueImproved PhaseSensitive OTDR for Breakdown Discharge Detection of GasInsulated Switchgear In this paper, we propose and demonstrate a gasinsulated switchgear (GIS) breakdown discharge detection system based on improved phasesensitive optical time domain reflectometry (xcfx86OTDR) assisted by 3Dprinted sensing elements. The sensing element is manufactured by a material with a high Poisson ratio for enhancement of the sensitivity of xcfx86OTDR to the acoustic emission detection during the breakdown discharge process. In our experiment, seven 3Dprinted sensing elements incorporating with optical fibers are attached tightly onto the shell of the GIS, which are monitored by xcfx86OTDR to localize and detect the acoustic emission signal resulted from the breakdown discharge. Ultimately, thanks to the phase demodulation, acoustic signals induced by the breakdown discharge process can be captured and recovered. Furthermore, the time delay analysis of detected signals acquired by different sensing elements on the GIS breakdown discharge unit is able to distinguish the location of the insulation failure part in the GIS unit. It suggests that the xcfx86OTDR incorporated with 3D printing technology shows the advantage of robustness in GIS breakdown discharge monitoring and detection.