Data-driven predictive maintenance method for digital welding machines

Li, Xing-chen; Chang, Dao-fang; SUN, You-gang

doi:10.1590/1517-7076-RMAT-2023-0096

Xing-chen Li

Shanghai Maritime University, Institute of Logistics Science and Engineering. 201306, Shanghai, China.

Dao-fang Chang

Shanghai Maritime University, Logistics Engineering College. 201306, Shanghai China.

You-gang SUN

Tongji University, Institute of Rail Transit. 201804, Shanghai, China.

https://orcid.org/0000-0002-1549-0108

e-mail: 202030510025@stu.shmtu.edu.cn, dfchang@shmtu.edu.cn, 1989yoga@tongji.edu.cn

Thumbnail

Figure 1.
Remaining service life of different maintenance methods.

Thumbnail

Figure 2.
The overall architecture diagram of the IA-LSTM-Attention model.

Thumbnail

Figure 3.
LSTM prediction model.

Thumbnail

Figure 4.
Attention Mechanism.

Thumbnail

Figure 3.
LSTM prediction model.

Thumbnail

Figure 4.
Attention Mechanism.

Thumbnail

Figure 5.
Relationship between maintenance cost per unit time and maintenance times.

Thumbnail

Figure 6.
Predictive Maintenance Decision Flow Chart.

Thumbnail

Figure 7.
Schematic diagram of remaining service life change of equipment during operation.

Thumbnail

Figure 8.
Comparison chart of the predicted results and true values of the four models for welding machine 1.

Thumbnail

Figure 9.
Comparison chart of the predicted results and true values of the four models for welding machine 2.

Thumbnail

Figure 10.
Comparison charts of absolute errors for the four models of welding machine 1.

Thumbnail

Figure 11.
Comparison charts of absolute errors for the four models of welding machine 2.

Thumbnail

Table 1.
Symbol description.

Thumbnail

Table 2.
Key indicators information of digital welding machine.

Thumbnail

Table 3.
Basic product information.

Thumbnail

Table 4.
Evaluation indicators and models of different prediction models.

Thumbnail

Table 5.
Predictive Maintenance Best Practices.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

(17)

(18)

(19)

(20)

(21)

(22)

(23)

(24)

(25)

(26)

(27)

(28)

(29)

(30)

(31)

(32)

(33)

(34)

(35)

(36)

(37)

(38)

(39)

(40)

CHARACTER	MEANING	CHARACTER	MEANING
r_ct	Maintenance cost per unit time	m	Product type
R_i (t)	Reliability	R_f	Reliability threshold
pv_m	Hourly production value of product m	p	Probability of secondary failure before completion of product m
pc_m	Hourly production cost of product m	ct	Completion time of product m
np	Production number of product m	C_tv	Total value of production
c_tc	Total production cost	cs	Cost per minimum maintenance
k	Number of random failures	d_i	Cost factor of the i preventive maintenance
c ₁	Minimum maintenance cost	cr	Cost of perfect repair
nr	Perfect repair times before completing product m operation	C₂	Maintenance cost of perfect maintenance
a_i	Decreasing factor of remaining service life of equipment	b_i	Failure rate increasing factor
t	Time variable of reliability time integration	c_i	Time factor of the i preventive maintenance
c ₄	Rework cost of product quality inspection	C₃	Cost of imperfect repair
t_r	Replacement time for perfect maintenance	t_s	Minimum maintenance replacement time
τ_i	Time of the i-th imperfect predictive maintenance	t_rp	Repair processing time of defective products
c_p	Penalty cost per unit time delay	C₅	Penalty cost for delivery delay
C_f	Fixed sampling cost in the production process of product m	C_v	Unit sampling cost
C_c	Sampling cost	n	sample size
sn_m	Sampling times during predictive maintenance of product m	p_d	Probability of product quality problems
c_m	Repair cost of product m

	UNIT	NUMERICAL RANGE
actual current	A	0–399
actual voltage	V	0–828
given current	A	0–439
given voltage	V	0–455
wire feeding speed	0.1 m/min	1.1–16.1

PRODUCT	PRODUCTION PROFIT	PRODUCTION COSTS	DELIVERY DELAY PUNISH	REVERSE COST	PROCESSING COMPLETION TIME
A	200	80	100	50	30
B	300	200	200	150	40
C	100	50	50	30	20

MODEL	MSE		MAE		RMSE		MAPE		R2
MODEL	WELDING MACHINE 1	WELDING MACHINE 2	WELDING MACHINE 1	WELDING MACHINE 2	WELDING MACHINE 1	WELDING MACHINE 2	WELDING MACHINE 1	WELDING MACHINE 2	WELDING MACHINE 1	WELDING MACHINE 2	WELDING MACHINE 1	WELDING MACHINE 2
ARIMA	0.0551	0.0647	0.2277	0.3065	0.2348	0.3579	533.58%	572.4%	–5.4513	–5.782
RNN	0.003552	0.005284	0.051	0.074	0.0596	0.0845	116.2%	120.7%	0.7225	0.7026
LSTM	0.000179	0.001895	0.0105	0.0362	0.0134	0.0489	12.84%	14.35%	0.7808	0.7534
IA-LSTM-Attention	0.000115	0.000945	0.0090	0.0124	0.0107	0.0238	3.73%	5.04%	0.8925	0.8543

MAINTENANCE SERIAL NUMBER	MAINTENANCE CYCLE (HOURS)	MAINTENANCE STRATEGY	MAINTENANCE TIME (HOURS)	EQUIPMENT RELIABILITY AFTER MAINTENANCE	MAINTENANCE COST PER UNIT TIME(YUAN)
1	100	Incomplete maintenance	3	0.8	9.5
2	90	Incomplete maintenance	3.5	0.76	9
3	84	Perfect maintenance	5	0.7	8
4	100	Incomplete maintenance	3	0.8	9.5

[1] e-mail: 202030510025@stu.shmtu.edu.cn, dfchang@shmtu.edu.cn, 1989yoga@tongji.edu.cn

Brasil

Brasil

Data-driven predictive maintenance method for digital welding machines

ABSTRACT