Underground pipelines are vital means of transporting fluid resources like water, oil and gas. The process of locating buried pipelines of interest is an essential prerequisite for pipeline maintenance and repair. Acoustic pipe localization methods, as effective trenchless detection techniques, have been implemented in locating underground utilities and shown to be very promising in plastic pipeline localization. This paper presents a comprehensive review of current acoustic methods and recent advances in the localization of buried pipelines. Investigations are conducted from multiple perspectives including the wave propagation mechanism in buried pipe systems, the principles behind each method along with advantages and limitations, representative acoustic locators in commercial markets, the condition of buried pipes, as well as selection of preferred methods for locating pipelines based on the applicability of existing localization techniques. In addition, the key features of each method are summarized and suggestions for future work are proposed. Acoustic methods for locating underground pipelines have proven to be useful and effective supplements to existing localization techniques. It has been highlighted that the ability of acoustic methods to locate non-metallic objects should be of particular practical value. While this paper focuses on a specific application associated with pipeline localization, many acoustic methods are feasible across a wide range of underground infrastructures.

In leak noise correlation surveys, time delay estimation (TDE) is of great importance in pinpointing a suspected leak. Conventional TDE methods involve pre-filtering processes prior to performing cross-correlation, based on a priori knowledge of the leak and background noise spectra, to achieve the desired performance. Despite advances in recent decades, they have not proven to be capable of tracking changes in sensor signals as yet. This paper presents an adaptive phase transform method based on least mean square (LMS) algorithms for the determination of the leak location to overcome this limitation. Simulation results on plastic water pipes show that, compared to the conventional LMS method, the proposed adaptive method is more robust to a low signal-to-noise ratio. To further verify the effectiveness of the proposed adaptive method, an analysis is carried out on field tests of real networks. Moreover, it has been shown that by using the actual measured data, improved performance of the proposed method for pipeline leakage detection is achieved. Hence, this paper presents a promising method, which has the advantages of simple implementation and ability to track changes in practice, as an alternative technique to the existing correlation-based leak detection methods.

This article presents a plastic pipeline leak localization method based on wavelet packet decomposition and higher order cumulants. The hydrophone sensors are used to capture the acoustic signals generated by the water leakage in the plastic pipe. The feature frequency band containing the most leak information is subsequently extracted by performing wavelet packet decomposition on the signals. The most suitable wavelet packet basis function for leak signals is obtained by comparing the effects of varieties of wavelet packet basis functions. Furthermore, the higher order cumulant method is used to estimate the time delay between the two sensor signals, from which the leak hole can be located. The simulation and experimental results show that, compared with the basic cross correlation method (BCC) and the generalized cross correlation method (GCC), the higher order cumulant method has higher leak localization accuracy. The relative error of the third-order cumulant method is 2.99% in experiment 1 and 7.58% in experiment 2, while the relative localization error of the fourth-order cumulant method is 1.96% in experiment 1 and 1.23% in experiment 2.

This article is concerned with a hyperboloid 3-D localization method of the transformer patrol robot based on the empirical mode decomposition (EMD) of ultrasonic signals and phase transform-beta (PHAT- β) generalized cross correlation (GCC). A linear sensor array is first employed to receive ultrasonic signals emitted by robot. The received signals are decomposed into several intrinsic mode functions (IMFs) of different scales by EMD. According to the correlation degree between IMFs and the received signals, the IMFs with large correlation coefficients are subsequently selected to reconstruct the signals. Furthermore, the PHAT- β GCC algorithm is used to estimate the time delay between the different sensors. Finally, multiple hyperboloids formed by sensors are intersected to accurately localize the robot. The simulation and experimental results demonstrate that the maximum relative errors in three typical positions are 5.6%, 4.8%, and 5.2%. The maximum localization error is less than 15 mm in a laboratory scale model with a size of length 800 mm, width 450 mm, and height 500 mm.