No abstract

No abstract

No abstract

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 critical impacts. A program of experimental studies on the model of a turbine plant with simulation of faults and receiving vibration signals has been developed. An experiment based on the homostatic method of checking the signal with Hamming windows, in the frequency, time and modulation domains and common initial data, allows one to determine the most promising signal characteristics for identification. A method has been developed for monitoring the state of turbine generators in an automatic mode for timely notification of the CHPP personnel about the appearance of signs of pre-emergency situations, as well as about the nature of faults by the method of predicting the state of a pre-emergency situation using convolutional neural networks implemented in the form of a recurrent autoencoder. Clustering is applied and clusters are identified that correspond to the spectrograms of pre-emergency situations. The effectiveness of the use of the homostatic method in combination with correlation analysis is based on the decision-making model described in more detail in other works.

The paper considers the issues of automatic classification of vibrational states of aircraft engine malfunctions based on the use of convolutional neural network processing of vibrational measurement data presented in spectral form and the knowledge of experts with experience in interpreting spectrograms characterizing the vibrational state of aircraft engines. The developed spectrogram analysis model allows the state monitoring of aircraft engines in automatic mode both during maintenance and in flight operation. The system is able to timely notify technical personnel or crew about the appearance of signs of emergency situations, as well as the type of possible malfunctions. It is shown that the main problem affecting the quality of detection of a potential turbine malfunction is a small sample of data corresponding to malfunctioning states. It is proposed to detect emission anomalies in a small sample by recognizing a modified wavelet transform and neural network clustering, which allows more complete formation of a training sample. The data samples used in training the neural network classifier during the experimental studies were generated on the basis of existing archive files containing complete aperture data from engine vibration sensors and information about malfunctions detected in them.