CNC machine gearbox fault detection with convolutional neural network
Department of Robotics and Complex Automation, Bauman Moscow State Technical University (BMSTU), Moscow, Russia.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2024, 11(02), 379–388.
Article DOI: 10.30574/wjaets.2024.11.2.0044
Publication history:
Received on 24 February 2024; revised on 06 April 2024; accepted on 09 April 2024
Abstract:
This study focuses on the development of a deep learning-based approach of gearbox monitoring and fault detection. The project aims to create a solution for early detection of defects in dynamic equipment based on data from vibration sensor by building a binary classifier with convolutional neural network implemented. The gearboxes condition of which is being assessed is stored in three similar computer numerically controlled (CNC) milling machines. Data is collected during 15 milling operations of different duration and with different tool’s speed and feed. Vibration is measured by an accelerometer stored on the body of each gearbox. Convolutional neural network takes vibration spectra as inputs and whether fault is detected makes a prediction of a gearbox condition. To make the whole solution autonomous and be able to embed it into manufacture the project is integrated into a server with an edge-to-cloud architecture. As an end product deep learning fault classifier stored on a server is to detect possible gearbox faults, draw conclusions on condition of dynamic equipment and automate the process of fault detection.
Keywords:
Gearbox; CNC machine; Vibration spectrum; Machine learning; Deep learning; Convolutional neural network
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0