Using neural networks to monitor piping systems

The paper proposes a state estimation technique, which uses Artificial Neural Networks (ANN) to monitor the status of piping networks carrying hazardous fluids, in order to identify and locate spills and leakages. A Multilayer Perceptron ANN is used to process pressure and flow rate information coming from a limited number of sensors distributed across the network.. The ANN is trained on different sets of input data; which characterize several states of the fluid network under normal and abnormal operating conditions. During the running phase, it acts as a classifier in order to estimate the actual system status and pinpoint leaks, based on available information, thereby solving the stated inverse problem. A two-level architecture is selected, composed of a the branch in which main ANN at the first level, to identify the leakage occurs, and several branch-specific ANNs at the second-level to accurately estimate the magnitude and location of the leaks. After describing the proposed methodology and the system architecture, we present a realistic application example in order to show the techniques potential.