Abstract: An apparatus for the continuous monitoring of a pipeline or a pipeline network carrying flowing media that can not only detect the presence of a leak but also locate the source of the leak through the use of rarefraction wave detection and methods of using the same is disclosed within. The apparatus and method are specifically configured to locate the leak source within less than 36 inches using a calibration means and a noise cancellation means.

Abstract: An apparatus for the continuous monitoring of a pipeline or a pipeline network carrying flowing media that can not only detect the presence of a leak but also locate the source of the leak through the use of rarefraction wave detection and methods of using the same is disclosed within. The apparatus and method are specifically configured to locate the leak source within less than 36 inches using a calibration means and a noise cancellation means.

Abstract: An apparatus for the continuous monitoring of a pipeline or a pipeline network carrying flowing media that can not only detect the presence of a leak but also locate the source of the leak through the use of rarefraction wave detection and methods of using the same is disclosed within. The apparatus and method are specifically configured to locate the leak source within less than 36 inches using a calibration means and a noise cancellation means.

Abstract: Provided herein are systems and methods to detect pipeline leaks. The systems and method can identify a pipeline pressure surge by applying a trained convolutional neural network (CNN) model for classifying pipeline pressure measurement images on each sensor site of a plurality of sensor sites, transfer pressure surge information obtained from at least a portion of the plurality of sensor sites to a cloud site, and determine whether the identified pressure surge is a pipeline leak at the cloud site using the pressure surge information. The plurality of sensor sites collect pipeline pressure measurement data. The pressure surge information corresponds to the identified pipeline pressure surge.

Abstract: Provided herein are systems and methods to detect pipeline leaks. The systems and method can identify a pipeline pressure surge by applying a trained convolutional neural network (CNN) model for classifying pipeline pressure measurement images on each sensor site of a plurality of sensor sites, transfer pressure surge information obtained from at least a portion of the plurality of sensor sites to a cloud site, and determine whether the identified pressure surge is a pipeline leak at the cloud site using the pressure surge information. The plurality of sensor sites collect pipeline pressure measurement data. The pressure surge information corresponds to the identified pipeline pressure surge.

Abstract: An apparatus for the continuous monitoring of a pipeline or a pipeline network carrying flowing media that can not only detect the presence of a leak but also locate the source of the leak through the use of rarefraction wave detection and methods of using the same is disclosed within. The apparatus and method are specifically configured to locate the leak source within less than 36 inches using a calibration means and a noise cancellation means.