Dual-tree complex wavelet transform and SVD based acoustic noise reduction and its application in leak detection for natural gas pipeline

Dual-tree complex wavelet transform and SVD based acoustic noise reduction and its application in leak detection for natural gas pipeline

Acoustic wave method

Dual-tree complex wavelet transform

Natural gas pipeline

Noise reduction

Singular value decomposition.

During the last decades, leak detection for natural gas pipeline has become one of the paramount concerns of pipeline operators and researchers across the globe. However, acoustic wave method has been proved to be an effective way to identify and localize leakage for gas pipeline. Considering the fact that noises inevitably exist in the acoustic signals collected, noise reduction should be enforced on the signals for subsequent data mining and analysis. Thus, an integrated acoustic noise reduction method based on DTCWT and SVD is proposed in this study. The method is put forward based on the idea that noise reduction strategy should match the characteristics of the noisy signal. According to previous studies, it is known that the energy of acoustic signals collected under leaking condition is mainly concentrated in low-frequency portion (0–100Hz). And ultralow-frequency component (0–5Hz), which is taken as the characteristic frequency band in this study, can propagate a relatively longer distance and be captured by sensors. Therefore, in order to filter the noises and to reserve the characteristic frequency band, DTCWT is taken as the core to conduct multilevel decomposition and refining for acoustic signals and SVD is employed to eliminate noises in non-characteristic bands. Both simulation and field experiments show that DTCWT-SVD is an excellent method for acoustic noise reduction. At the end of this study, application in leakage localization shows that it becomes much easier and a little more accurate to estimate the location of leak hole after noise reduction by DTCWT-SVD.

266-285

72-73

Yu, Xuchao

Liang, Wei

Zhang, Laibin

Jin, Hao

Qiu, Jingwei

Mechanical Systems and Signal Processing

2016

journalArticle

0888-3270

10.1016/j.ymssp.2015.10.034

https://www.sciencedirect.com/science/article/pii/S088832701500494X