Fluid pipe network intelligent detection and warning

Abstract

With the construction of smart mines, intelligent detection methods and technologies have become particularly important. An intelligent detection and early warning system has been proposed for leaks caused by factors such as corrosion, aging, and unintentional destruction of underground coal mine pipe networks. The negative pressure wave detection method was used to solve the difficult detection of complex environmental background, and MATLAB was used to extract noise features to improve operational efficiency. The generalized cross-correlation time delay estimation was not used to improve positioning accuracy. use the theoretical basis of deep sparse filter learning to identify leaks, and achieve accurate classification and identification of leaks by quickly and effectively extracting data features; use fixed base stations under mines as points to implement rapid LAN publish, So that relevant persons in charge can grasp the leaked information and take different response measures in a timely manner.