Data sharing method of industrial internet of things based on federal incremental learning

doi:10.11772/j.issn.1001-9081.2021071182

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (4): 1235-1243.DOI: 10.11772/j.issn.1001-9081.2021071182

Special Issue: CCF第36届中国计算机应用大会 (CCF NCCA 2021)

• The 36 CCF National Conference of Computer Applications (CCF NCCA 2020) • Previous Articles Next Articles

Data sharing method of industrial internet of things based on federal incremental learning

Jing LIU¹^,²^,³, Zhihong DONG¹, Zheyu ZHANG¹, Zhigang SUN⁴, Haipeng JI²^,³^,⁵()

^1.School of Artificial Intelligence，Hebei University of Technology，Tianjin 300401，China
^2.Hebei Data Driven Industrial Intelligent Engineering Research Center （Hebei University of Technology），Tianjin 300401，China
^3.Tianjin Development Zone Jingnuo Data Technology Company Limited，Tianjin 300401，China
^4.Tianjin HAVEL Branch，Tianjin Great Wall Motor Company Limited，Tianjin 300462，China
^5.School of Materials Science and Engineering，Hebei University of Technology，Tianjin 300401，China

Received:2021-07-08 Revised:2021-09-03 Accepted:2021-09-06 Online:2021-09-18 Published:2022-04-10
Contact: Haipeng JI
About author:LIU Jing， born in 1979， Ph. D.， research fellow. Her research interests include industrial artificial intelligence.
DONG Zhihong， born in 1987， M. S. candidate. His research interests include big data and intelligent computing， federated learning.
ZHANG Zheyu， born in 1997， M. S. candidate. His research interests include big data and intelligent computing， blockchain.
SUN Zhigang， born in 1982， assistant engineer. His research interests include lean manufacturing.
Supported by:
Hebei Natural Science Foundation(F2019202062)

基于联邦增量学习的工业物联网数据共享方法

刘晶¹^,²^,³, 董志红¹, 张喆语¹, 孙志刚⁴, 季海鹏²^,³^,⁵()

^1.河北工业大学人工智能与数据科学学院，天津 300401
^2.河北省数据驱动工业智能工程研究中心（河北工业大学），天津 300401
^3.天津开发区精诺瀚海数据科技有限公司，天津 300401
^4.长城汽车股份有限公司天津哈弗分公司，天津 300462
^5.河北工业大学材料科学与工程学院，天津 300401

通讯作者: 季海鹏
作者简介:刘晶（1979—），女，内蒙古包头人，研究员，博士，CCF高级会员，主要研究方向：工业人工智能
董志红（1987—），男，河北邯郸人，硕士研究生，主要研究方向：大数据与智能计算、联邦学习
张喆语（1997—），男，黑龙江哈尔滨人，硕士研究生，主要研究方向：大数据与智能计算、区块链
孙志刚（1982—），男，河北保定人，助理工程师，主要研究方向：精益生产制造
基金资助:
河北省自然科学基金资助项目(F2019202062)

Abstract

Abstract:

In view of the large amount of new data in the Industrial Internet Of Things（IIOT） and the imbalance of data at the factory sub-ends， a data sharing method of IIOT based on Federal Incremental Learning （FIL-IIOT） was proposed. Firstly， the industry federation model was distributed to the factory sub-end as the local initial model. Then， the federal sub-end optimization algorithm was proposed to dynamically adjust the participating subset. Finally， the incremental weight of the factory sub-end was calculated through the federal incremental learning algorithm， thereby integrating the new state data with the original industry federation model quickly. Experimental results the Case Western Reserve University （CWRU） bearing failure dataset show that the proposed FIL-IIOT makes the accuracy of bearing fault diagnosis reached 93.15%， which is 6.18 percentage points and 2.59 percentage points higher than those of Federated Averaging （FedAvg） algorithm and FIL-IIOT of Non Increment （FIL-IIOT-NI） method， respectively. The proposed method meets the needs of continuous optimization of industry federation model based on industrial incremental data.

Key words: Industrial Internet Of Things (IIOT), federated learning, incremental learning, data imbalance, optimized sub-end

摘要：

针对工业物联网（IIOT）新增数据量大、工厂子端数据量不均衡的问题，提出了一种基于联邦增量学习的IIOT数据共享方法（FIL-IIOT）。首先，将行业联合模型下发到工厂子端作为本地初始模型；然后，提出联邦优选子端算法来动态调整参与子集；最后，通过联邦增量学习算法计算出工厂子端的增量加权，从而使新增状态数据与原行业联合模型快速融合。实验结果表明，在美国凯斯西储大学（CWRU）轴承故障数据集上，所提FIL-IIOT使轴承故障诊断精度达到93.15%，比联邦均值（FedAvg）算法和无增量公式的FIL-IIOT（FIL-IIOT-NI）方法分别提高了6.18个百分点和2.59个百分点，满足了基于工业增量数据的行业联合模型持续优化的需求。

关键词: 工业物联网（IIOT）, 联邦学习, 增量学习, 数据不均衡, 优选子端

CLC Number:

TP391.77

Jing LIU, Zhihong DONG, Zheyu ZHANG, Zhigang SUN, Haipeng JI. Data sharing method of industrial internet of things based on federal incremental learning[J]. Journal of Computer Applications, 2022, 42(4): 1235-1243.

刘晶, 董志红, 张喆语, 孙志刚, 季海鹏. 基于联邦增量学习的工业物联网数据共享方法[J]. 《计算机应用》唯一官方网站, 2022, 42(4): 1235-1243.

Figures/Tables 15

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编号	轴承位置	故障部位	故障直径/mm
1	None	None	0.000
2	Driver End	IRW	0.007
3	Driver End	IRW	0.014
4	Driver End	ORW	0.007
5	Driver End	ORW	0.014
6	Driver End	BA	0.007
7	Driver End	BA	0.014
8	Fan End	IRW	0.007
9	Fan End	IRW	0.014
10	Fan End	ORW	0.007
11	Fan End	ORW	0.014
12	Fan End	BA	0.007
13	Fan End	BA	0.014

编号	轴承位置	故障部位	故障直径/mm
1	None	None	0.000
2	Driver End	IRW	0.007
3	Driver End	IRW	0.014
4	Driver End	ORW	0.007
5	Driver End	ORW	0.014
6	Driver End	BA	0.007
7	Driver End	BA	0.014
8	Fan End	IRW	0.007
9	Fan End	IRW	0.014
10	Fan End	ORW	0.007
11	Fan End	ORW	0.014
12	Fan End	BA	0.007
13	Fan End	BA	0.014

网络层	权重		偏置
网络层	weight_hh	weight_ih	偏置
LSTM层	256 × 100	256 × 100	100
dense层1	256 × 64		64
dense层2	64 × 13		13

网络层	权重		偏置
网络层	weight_hh	weight_ih	偏置
LSTM层	256 × 100	256 × 100	100
dense层1	256 × 64		64
dense层2	64 × 13		13

F值	通信轮次	每轮时间/s	训练集准确率	测试集准确率
0.3	33	12.395	0.954 8	0.938 8
0.5	29	18.703	0.960 2	0.943 9
0.7	26	23.787	0.962 3	0.945 4
1.0	17	28.854	0.972 1	0.963 1

Data sharing method of industrial internet of things based on federal incremental learning

基于联邦增量学习的工业物联网数据共享方法

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Metrics

F 值	方法	工厂子端	不同通信轮次下的极值
F 值	方法	工厂子端	1	5	10	20	30
0.3	FIL-IIOT	Factory_0	3.127	0.841	0.507	1.432	0.767
		Factory_1	0.768	1.960	0.527	1.576	1.782
		Factory_2	2.547	1.482	1.653	2.276	2.520
		Factory_3	1.926	2.568	0.911	1.374	2.167
		Factory_4	0.202	2.962	2.120	1.096	0.796
		Factory_5	2.772	3.005	0.232	2.895	1.279
		Factory_6	2.352	0.628	2.937	1.652	1.581
		Factory_7	3.779	1.384	0.132	1.563	1.280
		Factory_8	3.003	2.370	0.523	1.057	1.527
		Factory_9	2.830	1.196	1.268	0.904	1.666
	FedAvg	Factory_0	2.760	0.276	3.096	1.994	1.896
		Factory_1	2.626	1.062	1.944	1.137	1.271
		Factory_2	2.683	1.303	3.588	1.273	2.037
		Factory_3	1.415	2.944	1.911	0.994	1.199
		Factory_4	3.185	2.477	1.042	1.810	0.301
		Factory_5	1.688	2.420	1.038	2.294	3.023
		Factory_6	2.151	2.709	2.737	1.252	2.332
		Factory_7	0.444	2.344	2.365	0.966	0.942
		Factory_8	0.499	1.463	2.148	0.167	2.233
		Factory_9	2.947	2.710	2.616	2.350	1.277
1.0	FedAvg	Factory_0	0.357	1.789	2.212	2.352	0.738
		Factory_1	2.352	0.598	2.570	1.657	2.679
		Factory_2	0.766	1.832	1.408	1.253	2.489
		Factory_3	2.594	0.495	0.458	1.632	2.829
		Factory_4	0.403	2.411	2.549	2.964	1.682
		Factory_5	1.386	1.394	2.096	1.823	2.205
		Factory_6	0.411	3.620	1.230	1.489	2.806
		Factory_7	2.235	1.879	1.745	0.861	0.859
		Factory_8	0.641	3.447	3.177	0.630	1.399
		Factory_9	2.587	2.300	2.871	1.981	2.778

F 值	方法	训练集准确率	测试集准确率	时间/s
1.0	FedAvg	0.968 7	0.944 2	754
0.3	FedAvg	0.933 6	0.917 7	353
0.3	FIL-IIOT	0.955 6	0.931 8	362

方法	训练精度	训练时间/s	测试精度	测试时间/s
FedAvg	0.882 2	722	0.869 7	83
FIL-IIOT-NI	0.921 4	746	0.905 6	68
FIL-IIOT	0.945 6	478	0.931 5	65

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