Opportunistic Maintenance Modeling of Finishing Rolls Considering Dismounting Constraints

doi:10.16183/j.cnki.jsjtu.2022.021

Abstract

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

CLC Number:

TH17

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.

Figures/Tables 15

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Example results

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

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Fig.5

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Tab.5

Comparison of results of different strategies

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

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References 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