基于光纤测温和小波降噪的污水管道检测方法

A Sewer Detection Method Based on Fiber-Optic Distributed Temperature Sensing and Wavelet Based Denoising

摘要:

建立了基于小波降噪的光纤感温数据背景噪声值识别和污水管网入流无干扰检测方法，并结合实际污水管道识别的动态入流入渗事件进行了验证。结果表明：不同降噪算法得到的背景噪声阈值范围较大，对照实际污水与雨水入流事件，阈值取约±0.3 ℃左右时的识别效果最佳；阈值调节方法为算法选择的主导因素，多级阈值调节相比不调节和单级调节具有明显优势。据此给出了小波函数、阈值估计方法和阈值调节方法的优化参数，以实现可靠的污水管道检测效果。

Abstract:

This paper proposed a method to determine background noise of fiber temperature sensing data based on wavelet denoising， and then detected in-sewer inflow events without disturbing sewer flow conveyance. This method proposed was validated using detected dynamic inflow events in an actual sewer system. It was found that different wavelet denoising algorithms provide background noises that span a wide range， and a noise threshold of about ±0.3 ℃ corresponds to the best identification of actual sewer and stormwater inflow events. Threshold rescaling is the dominant factor for algorithm employment， where the multi-level rescaling method is obviously superior to the non-rescaling and single-level rescaling method. Accordingly， optimized parameters for the wavelet denoising algorithm including wavelet function， threshold selection rules and threshold rescaling were proposed， to enhance the reliability of sewer detection.