面向数字加工监控的边云工艺协同迁移

doi:10.19665/j.issn1001-2400.2022.06.018

Abstract

Abstract:

Intelligent manufacturing is the inevitable trend in the development of high-end equipment,and intelligent operation and maintenance are of great significance for ensuring the quality and reliability of digital processing.In the discrete intelligent manufacturing process,process diversity is the key bottleneck restricting the construction and implementation of digital models for intelligent operation and maintenance.This paper proposes an edge-cloud collaborative process knowledge migration scheme,which integrates edge computing and cloud computing to realize the rapid evolution of intelligent operation and maintenance models.First,a parallel multi-scale convolutional network (PMsCNN) in the cloud is trained to abstractly model the degradation process of the equipment under the historical process plan.Then,the unlabeled data samples under the new process plan is used to carry out transfer learning,so that PMsCNN can adapt to the new process plan.For this reason,an improved maximum mean difference loss function is proposed to overcome the problem of data imbalance.Finally,the evolved PMsCNN is applied to edge devices,and intelligent device operation and maintenance are implemented online.By taking the performance operation and maintenance of the equipment core and basic parts as a research case,the advanced nature of the proposed process knowledge migration scheme is verified.Compared with the existing monitoring method based on deep learning,the test accuracy rate under the new process scheme is improved by more than 20%,which is better than that of the existing migration diagnosis method.

Key words: condition monitoring, transfer learning, edge-cloud collaboration, parallel multi-scale convolutional network, performance degradation

CLC Number:

TH17

CAO Xincheng, YAO Bin, HE Wangpeng, CHEN Binqiang, QING Tao. Edge-cloud collaborative transfer of process knowledge for digital manufacturing monitoring[J].Journal of Xidian University, 2022, 49(6): 152-163.

Figures/Tables 10

References 36

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工艺方案编号	径向切削深度/mm	每齿进给量/mm	主轴转速/(r·min^-1)
a	6	0.1	2 500
b	6	0.2	2 500
c	12	0.1	2 000
d	12	0.2	2 000

方法	a/b/c→d	b/c/d→a	a/b→c/d	a/c→b/d	c→d	b→d
既有模型直接应用	75.9	77.0	65.0	69.1	63.0	55.0
文中所提方法	92.0	92.0	89.1	90.0	87.1	85.9
准确率提升幅度	16.1	15.0	24.1	20.9	24.1	30.9

方法	a/b/c→d	b/c/d→a	a/b→c/d	a/c→b/d	c→d	b→d
PTL	90.0	89.8	86.7	86.2	82.3	79.8
TCA	85.4	85.3	83.7	83.1	76.2	74.8
FTFA	86.1	86.4	84.1	84.3	81.4	78.9
MTN	89.2	89.3	86.5	86.3	84.1	81.7
DASAE	87.9	87.3	85.4	85.7	82.9	79.3
文中方法	92.0	92.0	89.1	90.0	87.1	85.9

Edge-cloud collaborative transfer of process knowledge for digital manufacturing monitoring

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