Russian Federation
The article describes a study aimed at developing a system for technical monitoring of process equipment using neural network methods for classifying technical states based on the analysis of signal spectra measured using combined sensors of physical quantities. The sensor firmware implements various algorithms for digital signal processing, which allows unifying the hardware.
technical diagnostics, diagnostics by signal spectra, sensors, technical monitoring system
1. Yablokov A. E., Blagoveschenskiy I. G. Nauchno-prakticheskie osnovy sozdaniya avtomatizirovannyh sistem tehnicheskogo monitoringa i diagnostiki oborudovaniya zernopererabatyvayuschih predpriyatiy na baze neyrosetevyh metodov analiza dannyh: monografiya / I. G. Yablokov, A. E. Blagoveschenskiy. – M., 2022. – 221 c. EDN: https://elibrary.ru/NZFKNK
2. Yablokov A. E., Blagoveschenskiy I. G. Nauchno-prakticheskie osnovy sozdaniya avtomatizirovannyh sistem tehnicheskogo monitoringa i diagnostiki oborudovaniya zernopererabatyvayuschih predpriyatiy na baze neyrosetevyh metodov analiza dannyh / A. E. Yablokov, I. G. Blagoveschenskiy. - M.: MGUPP, 2022. 221 c. EDN: https://elibrary.ru/NZFKNK
3. Yablokov A. E., Zhila T. M. Primenenie SNS v vibrodiagnostike po spektrogrammam i veyvlet-skalogrammam signala // Izvestiya Tul'skogo gosudarstvennogo universiteta. Tehnicheskie nauki. 2021. (12). C. 452–456. DOI: https://doi.org/10.24412/2071-6168-2021-12-452-457; EDN: https://elibrary.ru/AYELJE
4. Yablokov A. E., Zhila T. M., Generalov S. A. Klassifikaciya tehnicheskogo sostoyaniya oborudovaniya po spektrogrammam vibracii s primeneniem SNS. – Petrozavodsk: MCNP «Novaya nauka», 2021. C. 9–17. EDN: https://elibrary.ru/JAMRLN
