A sample learning free φ-OTDR pattern recognition method for PIG tracking based on Stockwell transform and improved Hough transform

• A sample-learning-free φ-OTDR event recognition method based on invert-V pattern. • Stockwell transform is applied to mitigate random noise and interference fading. • A ρ -summation Hough transform is proposed to detect lines in space-temporal graph. • An X detection method is proposed according to the symmetry of invert-V pattern. In the long-range pipeline monitoring application of Phase-sensitive Optical Time Domain Reflectometer (φ-OTDR), instantaneous high-energy vibration event, e.g., PIG, generates a pair of invert-V pattern in the space-temporal graph (ST-graph), providing clear signature for event recognition. This paper proposed a sample learning free φ-OTDR event recognition method, where a ST-graph is treated as a series of one-dimensional (1D) signals and a grayscale image separately in the preprocessing step and pattern recognition step. A homemade φ-OTDR system is employed as the distributed sensor to collect real-field space-temporal graph data containing invert-V pattern. In the preprocessing step, the Stockwell transform is applied to each 1D temporal signal as filter, and normalization is then performed to mitigate the effects of interference fading. In the pattern recognition step, a ρ -summation Hough transform is proposed to detect the two edges of invert-V pattern, considering that lines in ST-graph possess a certain width. The invert-V pattern is recognized by detecting pattern shaped like “X”, the lower half of which is extracted as the recognition result. Experimental result proves the feasibility of the proposed method of accurately capturing small invert-V patterns in the space-temporal graph.