Russian Federation
Russian Federation
The work conducts an experiment to isolate a useful signal from a correlated one (signal + noise) using the adaptive filtering method, evaluates its effectiveness, and the possibility of its use for signal preprocessing in acoustic methods for determining leaks in a pipeline system. The results obtained showed that the adaptive filtration method can be used in acoustic methods for detecting leaks in pipeline systems.
adaptive filtering, signal, noise, leakage, pipeline
1. Alekhin M.Yu., Yanchenko A.Yu., Krymskij V.V. O prognozirovanii ekonomicheskogo ushcherba ot chrezvychajnyh situacij // Nauch.-analit. zhurn. «Vestnik S.-Peterb. un-ta GPS MCHS Rossii». 2012. № 2. S. 84–88. EDN PGEXBP.
2. Skrypnikova O.I., SHCHetka V.F. Sravnitel'nyj analiz metodov ocenki riskov avarij na ob"ektah transportirovki nefteproduktov // Nauch.-analit. zhurn. «Vestnik S.-Peterb. un-ta GPS MCHS Rossii». 2022. № 4. S. 20–33. EDN MQNDXP.
3. Taranchuk E.A., Grigoryan A.N. Teoreticheskie i prakticheskie aspekty innovacionnogo razvitiya v Rossijskoj federacii // Nauch.-analit. zhurn. «Vestnik S.-Peterb. un-ta GPS MCHS Rossii». 2016. № 2. S. 96–100. EDN WAZPCF.
4. Akimova N.V. Distancionnoe obnaruzhenie techej v truboprovodah // Geo-Sibir'. 2009. T. 2. S. 137–142. EDN PFSFIX.
5. Belyj V.L. Kombinirovannaya sistema uluchsheniya razborchivosti audio signala v agressivnoj shumovoj srede // Komp'yuternye sistemy i seti: materialy 50-j Nauch. konf. aspirantov, magistrantov i studentov, Minsk: Belorusskij gos. un-t inform. i radioelektron., 2015. S. 190–192.
6. NOIZEUS: A noisy speech corpus for evaluation of speech enhancement algorithms. URL: http://ecs.utdallas.edu/loizou/speech/noizeus/ (data obrashcheniya: 17.10.2023).
7. Kalambet Yu.A., Koz'min Yu.P., Samohin A.C. Fil'traciya shumov. Sravnitel'nyj analiz metodov // Analitika. 2017. № 5 (36). S. 88–101. DOIhttps://doi.org/10.22184/2227-572X.2017.36.5.88.101. EDN ZIVVAJ.
8. Abitov R.N., Selyugin A.S., Nizamova A.H. Problemy nadezhnosti raboty vodoprovodnyh setej naselennyh punktov // Energosberezhenie i vodopodgotovka. 2022. № 5. S. 9–14. EDN WWSZZS.
9. Haykin S. Adaptive Filter Theory. Pearson Education India, 2002.
10. Regalia P. Adaptive IIR filtering in signal processing and control. Routledge, 2018. DOI:https://doi.org/10.1201/9781315136653.
11. Korobkov A.A., Osipova O.S. Ocenka kachestva fil'tracii v zavisimosti ot harakteristik vhodnogo signala adaptivnogo fil'tra // Novye tekhnologii, materialy i oborudovanie aviakosmicheskoj otrasli: materialy Vseros. nauch.-prakt. konf. s mezhdunar. uchastiem. 2016. C. 548–552. EDN WMXKOX.
12. Andrew Ng. CS294A Lecture Notes. Sparse autoencoder. Stanford. URL: http://web.stanford.edu/class/cs294a/sparseAutoencoder.pdf (data obrashcheniya: 23.10.2023).
13. Sergienko A.B. Cifrovaya obrabotka signalov: uchebnik dlya vuzov. 2-e izd. SPb.: Piter, 2006.
14. Paulo S.R. Diniz Adaptive Filtering. Springer Nature Switzerland AG, 2020.
15. Sergienko A.B. Algoritmy adaptivnoj fil'tracii: osobennosti realizacii v MATLAB // Eksponenta Pro. Matematika v prilozheniyah. 2003. № 1. S. 18–28. EDN TAXZCF.
16. Budyldina N.V., Truhin M.P. Analiz metodov adaptivnoj fil'tracii slabyh signalov na fone moshchnyh pomekh // Komp'yuternyj analiz izobrazhenij: Intellektual'nye resheniya v promyshlennyh setyah (CAI-2016): sb. nauch. trudov po materialam I Mezhdunar. konf. / pod obshch. red. A.G. Tyagunova. Ekaterinburg: Izd-vo UMC UPI, 2016. S. 153–154. EDN XWSMXL
