No abstract

The stable and uninterrupted work of the energy sector enterprises largely determines the economic development of the country. At the same time, the reliability of energy systems and complexes depends on the technical condition of the energy transportation equipment. In this regard, there is an urgent need to monitor the technical condition of the equipment. Currently, there are various types of non-destructive testing used for diagnostics, but none of them is universal, and many of them only allow to find defects of a certain type. In this connection, there is a topical issue of improving the methods for assessing the technical condition of equipment during the transportation of energy carriers in energy systems and complexes. This paper presents an information and diagnostic complex that implements the proposed technical solution and tests the methodology.

Currently, acoustic methods are widely used as a way to detect pipeline leaks. This is due to the fact that the acoustic signal has sufficiently capacious information about the state of the pipeline. The effectiveness of acoustic monitoring depends on the correct extraction of this information from the diagnostic signal. Currently, there is a search for new, more effective methods for analyzing acoustic signals. The article proposes to apply the theory of fractals to determine pipeline leaks. One of the most accurate methods for determining the fractal dimension of time series is R/S analysis using the Hurst exponent. An experimental stand has been developed and created, which includes a steel pipeline with water circulating in it. Water leakage from the pipeline was simulated by installing discs with holes of different diameters. The discs were placed in a special fitting on the surface of the pipeline. Acoustic signals recorded from the pipeline surface at different leakages and water pressure were analyzed. A relationship has been established between the size of the leak and the Hurst exponent of acoustic signals. The proposed method is compared with spectral analysis. Empirical experience has proven that R/S analysis can be used to determine pipeline leaks, as well as their classification by size.

No abstract

No abstract