Identification of temporal anomalies of spectrograms of vibration measurements of a turbine generator rotor using a recurrent neural network autoencoder
A method is proposed for recognizing pre-emergency conditions of rotary installations based on the use of the Hamming window and advanced Deep Learning techniques in retrospective analysis of the results of accounting for the factors of operation of a turbine generator, diagnostics and control under...
Main Authors: | V. P. Kulagin, D. A. Akimov, S. A. Pavelyev, E. O. Guryanova |
---|---|
Format: | Article |
Language: | Russian |
Published: |
MIREA - Russian Technological University
2021-04-01
|
Series: | Российский технологический журнал |
Subjects: | |
Online Access: | https://www.rtj-mirea.ru/jour/article/view/305 |
Similar Items
-
Fault Diagnosis Based on an Approach Combining a Spectrogram and a Convolutional Neural Network with Application to a Wind Turbine System
by: Wenxin Yu, et al.
Published: (2018-09-01) -
Autoencoder-based anomaly root cause analysis for wind turbines
by: Cyriana M.A. Roelofs, et al.
Published: (2021-06-01) -
Accurate Bearing Fault Diagnosis under Variable Shaft Speed using Convolutional Neural Networks and Vibration Spectrogram
by: Minh Tuan Pham, et al.
Published: (2020-09-01) -
Vibration Spectrum Analysis for Indicating Damage on Turbine and Steam Generator Amurang Unit 1
by: Beny Cahyono, et al.
Published: (2017-12-01) -
Algorithm for predicting the vibrational state of a turbine rotor using machine learning
by: M. A. Bolotov, et al.
Published: (2020-05-01)