Gear Grinding Monitoring based on Deep Convolutional Neural Networks
| Autor: | Konstantinos Gryllias, Katrien Declercq, Chenyu Liu, Zhuyun Chen, Yannick Meerten, Alex Ricardo Mauricio, Yann Vonderscher |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry Deep learning 020208 electrical & electronic engineering Short-time Fourier transform Pattern recognition 02 engineering and technology Filter (signal processing) Convolutional neural network Gear manufacturing Grinding Power (physics) Vibration 020901 industrial engineering & automation Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Artificial intelligence business |
| Zdroj: | IFAC-PapersOnLine. 53:10324-10329 |
| ISSN: | 2405-8963 |
| DOI: | 10.1016/j.ifacol.2020.12.2768 |
| Popis: | Grinding plays a vital role in modern gear manufacturing industry while the need for high quality products is continuously increasing. A methodology for gear grinding monitoring, exploiting the power of Deep Learning architectures and 2D representations, is presented in this paper. Vibration signals, measured during the grinding process under healthy and faulty conditions, are classified with high accuracy. Three types of faults i.e., a high profile form error, a high lead error, and a high profile slope variation, have been emulated. The Short-Time Fourier Transform (STFT) of each vibration signal is calculated, and the 2D time-frequency representations are input to a Deep Convolutional Neural Network (DCNN) for classification. Different filter sizes are tested, and the classification accuracy of 95.0% has been achieved, demonstrating the efficiency of the methodology for gear grinding monitoring. |
| Databáze: | OpenAIRE |
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