Key Laboratory of Noise and Vibration Research, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2019 Jan 14;19(2):310. doi: 10.3390/s19020310.
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.
在泄漏噪声相关调查中，时间延迟估计 (TDE) 对于精确定位可疑泄漏源非常重要。传统的 TDE 方法在进行互相关之前涉及预滤波过程，这是基于对泄漏和背景噪声频谱的先验知识，以实现所需的性能。尽管近年来取得了进展，但它们尚未证明能够跟踪传感器信号的变化。本文提出了一种基于最小均方 (LMS) 算法的自适应相位变换方法，用于确定泄漏位置，以克服这一限制。在塑料水管上的仿真结果表明，与传统的 LMS 方法相比，所提出的自适应方法在低信噪比下更稳健。为了进一步验证所提出的自适应方法的有效性，对实际网络的现场测试进行了分析。此外，已经表明，通过使用实际测量数据，可以提高所提出方法在管道泄漏检测中的性能。因此，本文提出了一种很有前途的方法，该方法具有简单实现和能够跟踪实际变化的优点，是现有基于相关的泄漏检测方法的一种替代技术。
