Use of PVDF Wire Sensors for Leakage Localization in a FluidFilled Pipe The detection and location of pipeline leakage can be deduced from the time difference between the arrival leak signals measured by sensors placed at the pipe access points on either side of a suspected leak. Progress has been made in this area to offer a potential improvement over the conventional crosscorrelation method for time delay estimation. This paper is concerned with identifying suitable sensors that can be easily deployed to monitor the pipe vibration due to the propagation of leak noise along the pipeline. In response to this, based on the lowfrequency propagation characteristics of leak noise in our previous study, polyvinylidene fluoride (PVDF) wire sensors are proposed as a potential solution to detect the pipeline leak signals. Experimental investigations were carried out at a leak detection pipe rig built in the Chinese Academy of Sciences. Their performances for leak detection were shown in comparison with hydrophones. It is suggested that with special considerations given to aspects pertaining to nonintrusive deployment and low cost, the PVDF wire sensors are of particular interest and may lead to a promising replacement for commercial leak noise transducers.

Spatiotemporal correlations for damages identification and localization in water pipeline systems based on WSNs Abstract   Pipeline leakages may conduct large excessive costs for reparation, infrastructure damage combined with environmental pollution. Consequently, the security and maintenance of the pipeline infrastructure are one of the major preoccupations for searchers. The most suitable are these solutions based on wireless sensors networks. However, WSNs are susceptible to noise effect, material defaults and malicious attacks from intruders. Therefore, it is essential to identify potential events like a leak, as well as erroneous and malicious attacks as defected sensors, occurred on the network. For that propose, we have presented a distributed one class classification technique for outliers detection based on WSNs. In addition, we have searched to improve our classifier by using a centered ellipsoidal technique to classify data. Then, we have demonstrated the importance of our improved technique comparing to other classifiers. Likewise, we have investigated the notion of the relationship existing between closed neighboring nodes and the existing correlation between historical observations to identify damages sources. These improvements led to increase the detection accuracy level and decrease the false alarm percentage by respecting WSNs constraints such as energy consumption.