离散制造系统能耗动态建模与在线预测

doi:10.16182/j.issn1004731x.joss.21-1304

摘要/Abstract

摘要：

针对传统离散制造系统能耗建模方法难以适应工况复杂多变性的问题，提出一种基于实时数据的能耗在线动态建模方法。分析离散制造系统和加工设备运行机理确定能耗影响因素；提出一种可动态调节隐藏层节点数的在线贯序极限学习机算法来构建能耗模型，当有实时数据时可快速更新模型；引入伯恩斯坦不等式提高模型的数据筛选能力。通过仿真实验和对比，验证了该方法具有回归精度高、预测误差小且建模用时短的优点，可应用于离散制造系统能耗的动态建模与在线预测场景。

关键词: 离散制造, 能耗预测, 在线贯序极限学习机, 伯恩斯坦不等式

Abstract:

Aiming at the traditional energy consumption modeling methods of discrete manufacturing system being difficult to adapt to the complexity and variability of working conditions, an online dynamic energy consumption modeling method based on real-time data is proposed. The energy consumption affecting factors are determined by analyzing the operation mechanism of the discrete manufacturing system and equipment. An online sequential extreme learning machine algorithm that can dynamically adjust the number of hidden layer nodes is proposed to construct the energy consumption model. The real-time data can update the model quickly. Bernstein's inequality is introduced to improve the model data screening ability. The simulation experiment and the comparison show that the method has better regression accuracy, smaller prediction error and shorter modeling time, and can be applied to the dynamic modeling and online prediction scenarios of energy consumption of discrete manufacturing systems.

Key words: discrete manufacturing, prediction of energy consumption, online sequential extreme learning machine, Bernstein inequality

中图分类号:

TP391.9

陈威, 王艳, 纪志成. 离散制造系统能耗动态建模与在线预测[J]. 系统仿真学报, 2023, 35(4): 760-772.

Wei Chen, Yan Wang, Zhicheng Ji. Dynamics Modeling and Online Prediction of Energy Consumption of Discrete Manufacturing System[J]. Journal of System Simulation, 2023, 35(4): 760-772.

图/表 13

图1

图2

表1

表2

N2=1时算法测试结果

数据集	ELM			OSELM			IOSELM			BI-IOSELM
数据集	$R 2$	RMSE	t/s	$R 2$	RMSE	t/s	$R 2$	RMSE	t/s	$R 2$	RMSE	t/s
Energy Efficiency	0.887	3.052	3.641	0.891	3.065	0.188	0.891	3.016	0.250	0.923	2.664	0.266
Real Estate Valuation	0.504	7.929	1.516	0.501	7.854	0.163	0.571	7.454	0.188	0.637	7.113	0.227
Air Quality	0.957	67.998	340.980	0.949	66.619	1.297	0.900	66.139	1.318	0.961	63.845	2.406
Yacht Hydrodynamics	0.741	5.114	0.984	0.721	8.175	0.125	0.658	7.630	0.141	0.766	4.914	0.198
SkillCraft1 Master Table	0.974	9.951	40.875	0.942	9.744	0.500	0.838	8.592	0.503	0.986	7.283	0.641

表2

表3

N2=2时算法测试结果

数据集	ELM			OSELM			IOSELM			BI-IOSELM
数据集	$R 2$	RMSE	t/s	$R 2$	RMSE	t/s	$R 2$	RMSE	t/s	$R 2$	RMSE	t/s
Energy Efficiency	0.885	3.050	1.984	0.876	3.152	0.250	0.880	3.116	0.266	0.916	2.653	0.322
Real Estate Valuation	0.541	7.620	0.766	0.466	8.032	0.234	0.592	7.326	0.250	0.645	6.965	0.297
Air Quality	0.954	68.951	184.200	0.953	70.112	1.313	0.937	69.312	1.516	0.962	63.085	3.469
Yacht Hydrodynamics	0.738	6.896	0.547	0.632	7.830	0.141	0.641	7.758	0.172	0.826	5.838	0.188
SkillCraft1 Master Table	0.961	10.864	24.750	0.954	10.588	0.281	0.907	12.495	0.297	0.971	8.711	0.516

表3

表4

N2=3时算法测试结果

数据集	ELM			OSELM			IOSELM			BI-IOSELM
数据集	$R 2$	RMSE	t/s	$R 2$	RMSE	t/s	$R 2$	RMSE	t/s	$R 2$	RMSE	t/s
Energy Efficiency	0.889	3.000	1.594	0.876	3.157	0.219	0.882	3.094	0.234	0.902	2.835	0.313
Real Estate Valuation	0.520	7.321	0.578	0.517	7.767	0.203	0.281	7.271	0.225	0.638	7.018	0.291
Air Quality	0.956	67.393	122.530	0.952	69.556	0.329	0.953	69.225	0.391	0.960	64.476	2.984
Yacht Hydrodynamics	0.786	6.363	0.453	0.547	8.419	0.115	0.632	7.830	0.121	0.805	6.114	0.126
SkillCraft1 Master Table	0.964	9.652	15.844	0.968	9.217	0.219	0.892	10.038	0.328	0.969	9.069	0.507

表4

表5

测试数据

序号	$v$ /(m/min)	$f$ /(mm/r)	$a$ /mm	$n$ /(r/min)	$P$ /W
1	60	0.10	0.5	381.97	1 440.8
2	60	0.25	2.0	561.72	3 463.3
3	80	0.10	0.5	509.29	1 583.9
4	80	0.25	2.0	748.96	4 364.4
…
61	100	0.15	1.5	860.29	3 503.8
62	120	0.15	1.5	1 032.40	4 074.9
63	100	0.10	1.0	649.61	2 312.7
64	80	0.10	2.0	578.74	2 638.8

表5

图3

图4

表6

模型测试结果

算法模型	$R 2$	RMSE	t/s
DE-SVR	0.982 8	130.980	2.312 5
BPNN	0.978 1	121.100	5.203 1
CART	0.853 5	356.460	0.718 8
ELM	0.995 8	64.833	0.171 9
OSELM	0.989 9	99.627	0.078 1
IOSELM	0.989 3	102.800	0.125 0
BI-IOSELM	0.997 4	50.503	0.140 6

表6

表7

图5

表8

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算法模型	样本预测值
算法模型	最大相对误差	平均相对误差
DE-SVR	14.84	3.70
BPNN	22.80	5.74
CART	34.34	10.12
ELM	9.47	1.90
OSELM	11.41	2.74
IOSELM	13.21	3.26
BI-IOSELM	5.33	1.39

测试算法	测试数据1		测试数据2		测试数据3		测试数据4
测试算法	预测值	误差/%	预测值	误差/%	预测值	误差/%	预测值	误差/%
DE-SVR	3 593.5	2.56	3 868.9	5.06	2 226.7	3.72	2 758.8	4.55
BPNN	3 280.3	6.38	3 820.3	6.25	2 272.0	1.76	3 156.0	19.60
CART	4 666.7	33.18	4 666.7	14.52	2 135.2	7.68	3 266.5	23.79
ELM	3 598.6	2.71	4 099.5	0.60	2 296.4	0.70	2 513.6	4.74
OSELM	2 859.1	18.40	3 317.2	18.59	2 691.0	16.35	3 158.6	19.70
IOSELM	3 057.7	12.73	4 147.6	1.78	2 691.9	16.40	3 055.3	15.78
BI-IOSELM	3 541.1	1.06	4 058.3	0.41	2 328.1	0.67	2 632.4	0.24

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