Joint Policy Optimization of Quality Control, Condition-Based Maintenance and Spare Ordering for a Degradation System

doi:10.16183/j.cnki.jsjtu.2022.356

Abstract

Abstract:

A joint policy of quality control, condition-based maintenance and spare ordering is proposed for a degradation system subject to the delay time concept. First, considering the fact that product quality is largely dependent on system state, a two-stage inspection policy is proposed, in which the system state is detected during the initial deterioration process, but the product quality is checked after the defective state is found by an inspection. Then, based on the condition inspection information, quality information and failure information, the corresponding maintenance activity is chosen. Combining the state of the spare part when the system replacement is required, all possible events during an inspection interval are discussed and then a mathematical model of average cost rate is established. Afterwards, a simulation-based optimization approach coupling discrete event simulation and response surface methodology is devised to obtain a near optimal joint policy. Finally, a numerical example is provided to demonstrate the effectiveness and applicability of the proposed policy by comparing it with the comparative policy.

Key words: quality control, condition-based maintenance, spare ordering, delay time, simulation-based optimization

CLC Number:

TH17
O213

HAN Mengying, MA Shugang, YANG Jianhua, LI Wei, MA Zhichao. Joint Policy Optimization of Quality Control, Condition-Based Maintenance and Spare Ordering for a Degradation System[J]. Journal of Shanghai Jiao Tong University, 2024, 58(3): 361-370.

Figures/Tables 9

Fig.1

Tab.1

Fig.2

Fig.3

Fig.4

Tab.2

Correlation results of ANOVA for Model 1

因素	SS	d.f	MS	F-value	p-value
Model 1	325.26	9	36.14	40.51	< 0.000 1
T	36.69	1	36.69	41.13	0.000 4
M	75.89	1	75.89	85.08	< 0.000 1
R_P	0.033 9	1	0.033 9	0.038	0.851 1
TM	58.81	1	58.81	65.92	< 0.000 1
TR_P	0.049 8	1	0.049 8	0.055 8	0.82
MR_P	2.39	1	2.39	2.68	0.145 9
T²	61.81	1	61.81	69.29	< 0.000 1
M²	50.12	1	50.12	56.19	0.000 1
$R P 2$	24.23	1	24.23	27.16	0.001 2
Lack of Fit	4.28	3	1.43	2.9	0.164 8
Pure Error	1.96	4	0.491 2
Cor Total	331.5	16

Tab.2

Fig.5

Tab.3

Correlation results of ANOVA for Model 2

因素	SS	d.f	MS	F-value	p-value
Model 2	534.43	5	106.89	123.49	< 0.000 1
T	17.52	1	17.52	20.24	0.002 8
R_P	256.46	1	256.46	296.29	< 0.000 1
TR_P	35.12	1	35.12	40.58	0.000 4
T²	24.46	1	24.46	28.26	0.001 1
$R P 2$	125.89	1	125.89	145.44	< 0.000 1
Lack of Fit	4.47	3	1.49	3.75	0.117 3
Pure Error	1.59	4	0.397 7
Cor Total	540.49	12

Tab.3

Tab.4

Sensitivity analysis results of CF

C_F	(T^, M^, $R P *$ )	c(T^, M^, $R P *$ )	C(T^, M^, $R P *$ )	(T^, $R P $ )	c(T^, $R P $ )	C(T^, $R P $ )
3 100	(14, 2, 0.078)	90.381 0	91.257 6	(13, 0.076)	91.370 8	92.072 8
3 700	(14, 2, 0.076)	95.407 6	96.175 2	(13, 0.072)	96.708 8	97.084 8
4 300	(13, 2, 0.075)	100.021 1	100.518 2	(12, 0.069)	101.351 0	101.962 3
4 900	(13, 2, 0.065)	105.338 9	105.760 6	(12, 0.066)	106.364 0	106.226 3
5 500	(11, 3, 0.058)	109.960 4	110.274 5	(12, 0.06)	110.598 0	110.331 6

Tab.4

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符号	符号说明	符号	符号说明
P	产品生产率	Γ	时间阈值
C_IC	单位状态检测费用	C_IQ	单位质量检测费用(C_IQ > C_IC)
C_P	单位预防更换费用	C_F	单位故障更换费用
C_D	单位次品损失	C_S	等待备件的单位时间惩罚费用
C_R	备件订购费用	C_H	单位时间库存持有费用