METHOD FOR ANALYZING INPUT DATA FROM GEAR VIBRATIONS
DOI:
https://doi.org/10.15588/1607-3274-2025-2-13Keywords:
1D convolutional neural networks, multilayer perceptron, stochastic gradient descent, loss function, engine vibration analysis, helicopter performance, signal classification, neural network, machine learningAbstract
Context. The paper considers the problem of analyzing large data vectors for analyzing helicopter engine performance. This issue is crucial for improving the reliability and efficiency of modern aviation technologies.
Objective. To create a method for analyzing engine vibration data to achieve accurate classification of engine states based on vibration signals.
Method. The input data is analyzed, and a decision is made to create a neural network that is trained to recognize the class of the input vector. The neural network can work immediately and be configured for further training based on similar data. The program was implemented using a classical neural network method. The optimal weights and offsets are calculated with derivatives to minimize the loss function. The stochastic gradient descent (SGD) algorithm was used for optimization, and different activation functions were tested to find the best configuration. Choosing the right activation functions ensured maximum performance.
Results. The graphs of the input vectors show that vectors from the first class had more peaks, which helped facilitate classification. After applying this method, the accuracy was about 70–75%, which was insufficient for the task. To improve this, we enhanced the model structure and reconfigured the activation functions. With the new method, the neural network can classify the input vector with 100% accuracy.
Conclusions. This study presents an approach to analyzing engine vibration data for assessing performance. The scientific novelty lies in adapting a multilayer perceptron (MLP) for classifying vibration signals. The research shows that high accuracy can be achieved without deep architectures by optimizing the MLP. This method is universally applicable, eliminating additional model adaptation costs, which is crucial for industrial use. The practical significance is demonstrated through software and experiments, proving the effectiveness of the MLP for performance monitoring when model parameters and activation functions are properly adjusted
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Copyright (c) 2025 O. Y. Shalimov, O. O. Moskalchuk, O. M. Yevseienko
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