METHOD OF NEURAL NETWORK DETECTION OF DEFECTS BASED ON THE ANALYSIS OF ROTATING MACHINES VIBRATIONS
DOI:
https://doi.org/10.15588/1607-3274-2025-1-10Keywords:
defects, analysis, vibrations, rotating machines, neural network, ResNet50Abstract
Context. The paper proposes a solution to the urgent problem of detecting equipment defects by analyzing the vibrations of rotating machines. The object of study is the process of detecting defects by analyzing the vibrations of rotating machines. The subject of
study is artificial intelligence methods for detecting defects by analyzing the vibrations of rotating machines.
Objective. Improving the accuracy of detecting defects in the analysis of rotating machine vibrations by creating a method for neural network detection of defects in the analysis of rotating machine vibrations and a corresponding neural network model that can
detect defects in the analysis of rotating machine vibrations without removal preliminary noise in order to preserve important features for more accurate classification.
Method. A method of neural network defect detection based on the analysis of vibrations of rotating machines is proposed, which is capable of predicting the presence or absence of a defect based on the input data of vibrations with the implementation of preliminary processing, namely the creation of a two-dimensional time-frequency image. The method differs from the existing ones
in that the defect analysis is performed without removing noise by fine-tuning the model parameters.
Results. The proposed method of neural network detection of defects based on the analysis of rotating machines vibrations is implemented in the form of a web application and the effectiveness of the neural network model obtained by performing the steps of the method is studied.
Conclusions. The study results show that the model has achieved high accuracy and consistency between training and validation data, which is confirmed by high values of such indicators as Accuracy, Precision, Recall і F1-Score on the validation dataset, as well
as minimal losses. The cross-validation confirmed the stable efficiency of the model, demonstrating high averaged metrics and insignificant deviations from the obtained metrics. Thus, the neural network model detects defects in rotating machines with high efficiency even without cleaning vibration signals from noise. Prospects for further research are to test the described method and the resulting neural network model on larger data sets.
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