Effects of windowing filters in leak locating for buried water-filled cast iron pipes

Abstract

Arrival time difference or time delay estimation is critically important for detection of leak location in buried water supply pipes. Because the exact leak locating depends upon the precision of the arrival time difference estimation between leak signals measured by sensors and the propagation speed of the leak-related elastic wave, the research on the estimation of time delay has been one of the key issues in leak locating. The arrival time difference was estimated with the peak time of cross correlation functions of the measured signals. In this study six different window functions, including the basic rectangular, Roth, Wiener, SCOT, PHAT and maximum likelihood windows were applied. Experimental results against an actual buried pipe made of cast iron showed that the introduction of the window functions improved the precision of time delay estimation. In this paper, a new statistical approach, that combines all results of each window function, is suggested for better leak locating. Apart from the experiment, an intensive theoretical analysis in terms of signal processing is described.

Source location and anomaly detection for damage identification of buried pipelines using kurtosis-based transfer function

“…Here, the cross-correlation for estimating the arrival time difference, R xy ( τ ) , can be expressed as follows 39 :…”

“…In particular, the research was undertaken to improve the cross-correlation function for leak signal detection in the in-service waterworks pipelines, and a meaningful leak detection approach was established by selecting the optimal frequency band and cross-correlation function using the window filters. 39 Thus, the Roth processor (ROTH), 40 smoothed coherence transform (SCOT), 41 and cross-correlation function using the maximum likelihood (ML) 42 window filters have been experimentally validated for TDoA estimation.…”