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

摘要/Abstract

摘要：

智能制造是高端装备发展的必然趋势,设备智能运维对于保障数字加工质量及可靠性具有重要意义。在离散智能制造过程中,工艺多样性成为制约智能运维数字模型构建与实施的关键瓶颈。为此,提出一种边云协同的工艺知识迁移方案,融合应用边缘计算和云计算,实现智能运维模型的快速进化。首先,在云端训练PMsCNN模型,对历史工艺方案下设备的退化过程抽象建模;然后,利用新工艺方案下无标签的数据样本开展迁移学习,使PMsCNN适应新的工艺方案。相应地提出了一种改进型最大均值差异损失函数,克服数据不均衡难题;最后,将进化后的PMsCNN部署应用于边缘设备,在线实施设备智能运维。以设备核心基础件的性能运维为研究案例,验证了所提工艺知识迁移方案的先进性。相比于现有基于深度学习的监测方法,新工艺方案下测试准确率提升了20%以上,优于现有迁移诊断方法。

关键词: 状态监测, 迁移学习, 边云协同, 并行多尺度卷积网络, 性能退化

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

中图分类号:

TH17

曹新城, 姚斌, 贺王鹏, 陈彬强, 卿涛. 面向数字加工监控的边云工艺协同迁移[J]. 西安电子科技大学学报, 2022, 49(6): 152-163.

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.

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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