Acoustic Characterization of PVC Sewer Pipes for Crack Detection Using Frequency Domain Analysis
As an existing industry standard, cracks and other structural defects in sewer pipes are detected by passing a closed-circuit television (CCTV) camera mounted on remote-controlled crawler through the pipes. The video from the camera is carefully observed by a trained operator to classify the condition of the pipe. The operator annotates video from the camera with appropriate notes based on condition of the pipe. In addition to a crawler with a CCTV camera, the inspection system needs a vehicle capable of off-road travel, a dedicated power source, and a proprietary software with a customized control system. These requirements make the CCTV based system crew-hour intensive and expensive. There is a need to develop an easily deployable and cost-effective solution to detect cracks and other structural defects in sewer pipes. For smart cities, this application is important as any leaking toxic effluents from cracked pipes pose a direct public health hazard and degrades the environment. Recent developments in acoustic based pipe inspection technologies have demonstrated effectiveness in sewer blockage detection. These systems, however, are not capable of detecting cracks in sewer pipes. This paper reports on an acoustic based approach to detect cracks in polyvinyl chloride (PVC) sewer pipes. Extensive laboratory testing has been conducted on clean and cracked samples of 0.1-m diameter, 3.04 m long pipes. The pipes are excited with acoustic signals and their frequency response is analyzed to characterize difference between a clean and a cracked sample. The results indicate that acoustic signal attenuation from cracked pipe can be a good indicator of an existence of a structural defect. This study is ongoing and trials on live sewer installations will be conducted in near future. © 2018 IEEE.
2018 IEEE International Smart Cities Conference, ISC2 2018
Khan, M. S. and Patil, R. (2018). Acoustic characterization of PVC sewer pipes for crack detection using frequency domain analysis. Proceedings from 2018 IEEE International Smart Cities Conference (ISC2): 1-5. doi: 10.1109/ISC2.2018.8656739.