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| Fault Diagnosis System of Numerical Control Machine Based on Incremental Learning |
| ZHANG Yu-ying1,LU Yi1,ZHAO Jing2 |
1. College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou, Zhejang 310018, China
2. Hangzhou Wolei Intelligent Technology Co.Ltd, Hangzhou, Zhejang 310018, China |
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Abstract In order to solve the problem of fault diagnosis when the spindle bearing and tool in numerical control machine malfunction at the same time or working conditions of numerical control machine are changed, a fault diagnosis model named deep convolutional neural network based on incremental learning was proposed. First, the vibration data sets of spindle bearings and tools at common speeds were input into a one-dimensional convolutional neural network, which combined a batch normalization algorithm. Secondly, manually judged the unknown fault type during cross-speed diagnosis, tagged it and re-entered the network. Incremental learning was used to retain old knowledge and learn the characteristics of new data to improve model performance. The fault diagnosis accuracy rate of the model at different speeds is between 76.49% and 86.09%. Compared with the two classic cross-domain algorithms of fine tuning and joint training, deep convolutional neural network based on incremental learning improves accuracy and shortens training time.
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Received: 09 July 2021
Published: 14 November 2022
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2022, Vol. 43 
