磨料
流离失所(心理学)
材料科学
学习迁移
卷积神经网络
样品(材料)
人工神经网络
人工智能
计算机科学
质量(理念)
深度学习
机械工程
复合材料
工程类
哲学
认识论
化学
色谱法
心理治疗师
心理学
作者
Zhihang Li,Qian Tang,Sibao Wang,Penghui Zhang
标识
DOI:10.1016/j.jmapro.2021.12.022
摘要
The wear state of abrasive belts is directly related to the final processing quality and accuracy. Therefore, the accurate prediction of the replacement time of abrasive belts can help not only improve the product quality but also reduce the cost. According to the analysis of displacement data, a new method for the prediction of abrasive belt wear states using a multiscale convolutional neural network based on transfer learning is proposed. Initially, first-order difference preprocessing is ingeniously performed on displacement data. Then, the network parameters of the model are obtained by pretraining the fault dataset and are directly transferred or fine-tuned according to the preprocessed displacement data. Finally, the preprocessed displacement data corresponding to different abrasive belt wear states are accurately classified. This method verifies the application of transfer learning between cross-domain data in industry and resolves the contradiction between the large sample size required for deep learning and the difficulty of obtaining a large amount of sample data in actual production. The experimental results show that this method can accurately predict the wear status of abrasive belts, with an average prediction accuracy of 93.1%. This method has the advantages of low cost and easy operation, and can be applied to guide the replacement time of abrasive belts in production.
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