Multi-point disturbance detection is always challenging in polarization-sensitive optical time domain reflectometry (POTDR). In this paper, we propose a novel method to solve such a challenging problem by accumulating the temporally differentiated OTDR traces and building up a two-dimensional temporally-spatially evolving graph, and then using edge detection and automatic-clustering, adopted from imaging processing techniques, to discriminate different disturbance points and find out their locations. Many multi-point disturbance cases are tested and the results show that the method proposed has better performance than the conventional direct differentiation method and the Fast Fourier Transform (FFT) spectrum analysis. In particular, the location accuracy has been improved significantly.
With the growing demand for monitoring length and channel number of the fully distributed optical fiber sensors (DOFSs), the amount of sensing data is increasing rapidly, and there will be a heavy pressure for the massive data storage and transmission. In this paper, two lossless compression algorithms of Lempel-Ziv-Welch (LZW) and Huffman are comparatively studied to effectively compress the huge amount of data of typical DOFSs, e.g. Φ -OTDR, POTDR, and BOTDA systems. The comparison results show that the LZW based on dictionary has a better performance in the consuming time and compression ratio for the DOFS data.
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