Affiliation:
1. China Electronic Product Reliability and Environmental Test Institute, Guangzhou 510610, China
2. Key Laboratory of Industrial Equipment Quality Big Data, MIIT, Guangzhou 510610, China
Abstract
Remaining useful life prediction of a milling tool is one of the determinants in making scientific maintenance decision for the CNC machine tool. Predicting the RUL accurately can improve machining efficiency and the quality of product. Deep learning methods have strong learning capability in RUL prediction and are extensively used. Multiscale CNN, a typical deep learning model in RUL prediction, has a large number of parameters because of its parallel convolutional pathways, resulting in high computing cost. Besides, the MSCNN ignores various influences of different scales of degradation features on RUL prediction accuracy. To address the issue, a pyramid CNN (PCNN) is proposed for RUL prediction of the milling tool in this paper. Group convolution is used to replace parallel convolutional pathways to extract multiscale features without additional large number of parameters. And the channel attention with soft assignment is used to select the key degradation features, considering different sensors and scales. The milling tool wear experiments show that the score value of the proposed method achieved 51.248 ± 1.712 and the RMSE achieved 19.051 ± 0.804, confirming better performance of the proposed method compared with the traditional MSCNN and other deep learning methods. Besides, the number of parameters of the proposed method is reduced by 62.6% and 54.8% compared with the MSCNN with self-attention and the MSCNN methods, confirming its lower computing cost.
Funder
Key Research and Development Projects in Guangdong Province
Subject
Mechanical Engineering,Mechanics of Materials,Geotechnical Engineering and Engineering Geology,Condensed Matter Physics,Civil and Structural Engineering
Cited by
3 articles.
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