Machine Diagnostics in Mechatronic Systems: Analysis Methods and Intelligent Technologies
DOI:
https://doi.org/10.26906/znp.2025.64.4146Keywords:
machine diagnostics, machine learning, mechatronic systems, CNN, LSTM, signal processing, condition prediction, digital twins, vibration analysisAbstract
The article examines modern approaches to machine diagnostics within mechatronic systems using signal processing methods and intelligent machine learning technologies. The structure of mechatronic complexes is analyzed, and their specific features that influence the development of diagnostic models are identified, including the high level of interdependence between mechanical, electronic, and software components. The feasibility of applying hybrid diagnostic systems is substantiated, where convolutional neural networks (CNN) are employed for automatic extraction of informative features from vibration and sensor data, while recurrent networks such as LSTM provide analysis of the temporal dynamics of processes and prediction of degradation states. A generalized theoretical diagnostic model is proposed, combining spectral methods of preliminary signal processing, multisensor data integration, and modules for technical condition prediction. The obtained results demonstrate the high effectiveness of intelligent algorithms in detecting early signs of faults, even under noise disturbances and varying operating modes. The proposed approach can be applied in maintenance systems at industrial enterprises to enhance the reliability and extend the service life of mechatronic systems.
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Copyright (c) 2025 Serhii Oryschenko, Viktor Oryscshenko
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