考虑下机约束的精轧机组工作辊机会维护建模

doi:10.16183/j.cnki.jsjtu.2022.021

摘要/Abstract

摘要：

以钢铁精轧机组工作辊为对象,综合考虑轧辊磨损、机组两类故障、辊缝质量控制对故障损失、轧件质量损失与维护成本的影响,构建单辊维护模型以实现单辊维护周期的动态决策.在此基础上,考虑各轧辊磨削周期不同、下机周期相同、下机计划允许小幅调整等特殊场景,提出工作辊动态机会维护策略并构建决策模型,优化精轧辊组工作辊机会维护方案.实例分析表明,与传统的时间窗策略相比,动态机会维护策略在降低工作辊维护成本方面更具优势.

关键词: 精轧工作辊, 轧辊磨损, 磨削周期, 机会维护, 动态维护策略

Abstract:

A single roll maintenance model is established to realize the dynamic decision of single roll maintenance cycle by considering the influence of roll wear, unit faults, and roll gap quality control on accident loss, piece quality loss, and maintenance cost. Considering special scenarios such as different grinding cycles of each roll, the same unloading cycle and allowing small adjustment of dismounting schedule, a dynamic opportunity maintenance strategy of work roll is proposed and a decision model is constructed to optimize the work roll opportunity maintenance scheme of the finishing rolls. The case study shows that compared with the traditional time window strategy, the dynamic opportunity maintenance strategy has more advantages in reducing the maintenance cost of work roll.

Key words: finishing roll, roll wear, grinding period, opportunistic maintenance, dynamic maintenance strategy

中图分类号:

TH17

贲旭瑞, 周晓军. 考虑下机约束的精轧机组工作辊机会维护建模[J]. 上海交通大学学报, 2023, 57(8): 996-1004.

BEN Xurui, ZHOU Xiaojun. Opportunistic Maintenance Modeling of Finishing Rolls Considering Dismounting Constraints[J]. Journal of Shanghai Jiao Tong University, 2023, 57(8): 996-1004.

图/表 15

图1

图2

图3

图4

表1

表2

表3

实例结果

T_t/km	N_M	$N - M$	R_t(T_t)/(元·km^-1)
2 780	104	3.17	7 509

表3

表4

图5

图6

图7

表5

不同策略下的结果对比

策略	T_t/km	N_M	$N - M$	R_t(T_t)/(元·km^-1)
1	2 780	104	3.17	7 509
2	2 920	137	2.53	7 915
3	2 840	112	2.96	7 768

表5

表6

表7

表8

参考文献 18

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j	W_G/mm	W_max/mm	P_roll/元	p_M	C₁/元	C₂/元	C₃/元	C₄/元	D₀/mm	D₂/mm
1	1.8	2.0	20 500	0.99	11 800	15 300	35 100	9 700	655	535
2	1.8	2.0	20 500	0.99	11 800	15 300	35 100	9 700	655	535
3	1.8	2.0	20 500	0.99	11 800	15 300	35 100	9 700	655	535
4	1.6	1.5	26 000	0.99	11 800	15 300	35 100	9 700	650	530
5	1.4	1.2	32 000	0.99	11 800	15 300	35 100	9 700	630	510
6	1.4	1.2	32 000	0.99	11 800	15 300	35 100	9 700	630	510
7	1.4	1.2	32 000	0.99	11 800	15 300	35 100	9 700	630	510

j	A	ln G	α	(α₁,β₁)	(α₂,β₂)	k₁
1	2.83	N(-9.83, 0.91²)	N(0.83, 0.11²)	(81.7, 1.3)	(193.7, 1.9)	1.15
2	2.83	N(-9.59, 0.93²)	N(0.85, 0.13²)	(92.6, 1.4)	(184.2, 1.5)	1.17
3	2.83	N(-9.67, 0.85²)	N(0.87, 0.15²)	(79.6, 1.3)	(203.2, 2.3)	1.31
4	2.83	N(-9.92, 0.87²)	N(0.92, 0.17²)	(65.1, 1.5)	(164.3, 1.3)	1.19
5	2.83	N(-9.92, 0.99²)	N(0.96, 0.19²)	(113.2, 1.3)	(228.1, 1.4)	1.26
6	2.83	N(-9.94, 0.96²)	N(0.94, 0.16²)	(94.2, 1.4)	(195.7, 1.1)	1.21
7	2.83	N(-9.66, 0.87²)	N(0.96, 0.17²)	(85.3, 1.5)	(206.6, 1.8)	1.33

策略	R_t(T_t)/(元·km^-1)
策略	C_m	C_q	C_a	C₄
1	3 797	1 407	2 305	57
2	4 189	1 385	2 341	0
3	3 966	1 421	2 381	0

k'C₂	R_t(T_t)/(元·km^-1)
k'C₂	策略1	策略2	策略3
0.1	7 018	7 395	7 258
0.25	7 321	7 715	7 572
0.5	7 473	7 875	7 729
1	7 509	7 915	7 768
2	7 617	8 031	7 881
5	8 138	8 580	8 421

k'C₃	R_t(T_t)/(元·km^-1)
k'C₃	策略1	策略2	策略3
0.1	6 998	7 383	7 244
0.25	7 308	7 709	7 564
0.5	7 462	7 872	7 724
1	7 509	7 915	7 768
2	7 610	8 032	7 879
5	8 140	8 591	8 428