Diagnostic technique based on additive models in the tasks of the ongoing exploitation of gas network

In extensive fittings used for transporting large quantities of gas under high pressure for long distances, monitoring of the fittings condition becomes a significant problem, in respect of correct functioning of the measuring devices, as well as the occurrence of possible leakage. Exploitation of gas network requires periodic tightness controls and elimination of faults and leaks. When a leak is discovered in the gas pipeline, it undergoes repair work, which is conducted after shutting down a certain section of the network by shut-off valves or temporary closure. Works on active gas pipelines are considered hazardous and must be performed by qualified teams. Difficult conditions of exploitation are placing increasingly high demands on long duration and high degree of reliability of control systems. Due to flammability, any breakdowns causing unsealing of the fittings and gas effusion pose a risk of explosion and environmental contamination. These risks may be eliminated by current detection which enables prediction of the possible necessity of switching off pumping or excluding the leaky section of the pipeline. In the current exploitation of gas networks a number of solutions can be used allowing for monitoring and diagnostics, with particular consideration of leakage detection. The methods of detection of transmission networks can be divided into two general categories [2, 11, 21]: direct (external), when the detection is conducted from the outside of the pipe by applying specialized devices and visual observation, and indirect (internal), when the detection is based on the measurements and analysis of parameters of flow process, such as pressure, flow, temperature. Among the direct methods we can differentiate acoustic methods [12], which are based on the detection of noise generated by a leak and require installing acoustic sensors along the pipeline. Indirect methods are divided into methods based on detecting sound waves caused by effusion, methods based on balancing the medium inflowing toand outflowing from the pipeline and analytical methods based on mathematical model and measuring data of the object, obtained from telemetric system [7, 8, 17, 22]. Natural gas is a viscous and compressible gas, the physicochemical parameters of which are strongly dependent on pressure and temperature conditions. For description of such a medium, application of complicated equations of state is necessary, such as virial or cubic equations of state of the gas [5, 23]. The dynamics of elementary section of the gas pipeline can also be described by partial differential equations system [7, 17], which can be derived from mass and momentum conservation principles and solved by explicit or implicit methods. Optimization algorithms based on neural networks or swarm intelligence [1, 9, 16] can also be applied for the analysis of work of certain sections of transmission network. It is a technique of artificial intelligence based on the observation of social behavior in organized populations. To identify whether there is a leakage or not, a support vector machine (SVM) can be used, i.e. the algorithm identifying the relationship between the elements (measurement results, in this case) on the basis of the examples – sets of training data, comprising cases with and without a leakage [3]. Direct methods require vast experience from the operator of the device, thus fault detection services are each time contracted to specialized companies. On the other hand, indirect methods, in which the expert (company employee) observes network parameters and detect anomalies, have a number of disadvantages, significantly decreasing their value. First of all, the system doesn’t indicate faults automatically what demands continuous attention of an expert. On the other hand, Zofia M. ŁAbędA-GrudZiAk