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Frontiers of Mechanical Engineering

ISSN 2095-0233

ISSN 2095-0241(Online)

CN 11-5984/TH

Postal Subscription Code 80-975

2018 Impact Factor: 0.989

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Front. Mech. Eng.    2019, Vol. 14 Issue (4) : 412-421    https://doi.org/10.1007/s11465-019-0551-0
RESEARCH ARTICLE
Timing decision-making method of engine blades for predecisional remanufacturing based on reliability analysis
Le CHEN, Xianlin WANG, Hua ZHANG(), Xugang ZHANG, Binbin DAN
Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
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Abstract

A timing decision-making method for predecisional remanufacturing is presented. The method can effectively solve the uncertainty problem of remanufacturing blanks. From the perspective of reliability, this study analyzes the timing decision-making interval for predecisional remanufacturing of mechanical products during the service period and constructs an optimal timing model based on energy consumption and cost. The mapping relationships between time and energy consumption are predicted by using the characteristic values of performance degradation of products combined with the least squares support vector regression algorithm. Application of game theory reveals that when the energy consumption and cost are comprehensively optimal, this moment is the best time for predecisional remanufacturing. Used engine blades are utilized as an example to demonstrate the validity and effectiveness of the proposed method.
Keywords predecisional remanufacturing      reliability      least squares support vector regression (LS-SVR)      game theory     
Corresponding Author(s): Hua ZHANG   
Just Accepted Date: 30 August 2019   Online First Date: 29 September 2019    Issue Date: 02 December 2019
 Cite this article:   
Le CHEN,Xianlin WANG,Hua ZHANG, et al. Timing decision-making method of engine blades for predecisional remanufacturing based on reliability analysis[J]. Front. Mech. Eng., 2019, 14(4): 412-421.
 URL:  
https://academic.hep.com.cn/fme/EN/10.1007/s11465-019-0551-0
https://academic.hep.com.cn/fme/EN/Y2019/V14/I4/412
Fig.1  Schematic of the timing decision of predecisional remanufacturing based on reliability.
Fig.2  Simplified steps of the timing decision-making method based on reliability analysis.
Fig.3  Energy consumption of products in their life cycle.
Fig.4  Schematic of remanufacturing cost and energy consumption over time.
Fig.5  Schematic of the cost prediction process.
Fig.6  Flow chart of the time-average cost prediction algorithm for used mechanical products.
NO. Usage time/h Status
1 7221 F
2 7356 S
3 7433 F
4 7561 S
5 7589 S
6 7680 S
7 7766 F
8 7951 S
9 7865 F
10 8025 S
… … …
65 14105 S
Tab.1  Engine blade fault data sheet
No. Time/h Input xi Output y
x1 x2 x3
1 6903 0.015334 0.036177 0.001631 472.17
2 6906 0.016339 0.068936 0.003150 464.84
3 6940 0.158180 0.228303 0.005582 442.49
4 6965 0.213205 0.228849 0.006254 434.91
5 6978 0.317898 0.245257 0.006706 419.57
… … … … … …
20 7709 1.878602 1.125149 0.018714 150.00
Tab.2  Training sample data set U
No. Time/h Input xi
x1 x2 x3
1 6915 0.028751 0.070302 0.005247
2 6923 0.120918 0.169003 0.005342
3 6980 0.362685 0.341557 0.006751
4 6995 0.391866 0.384221 0.006814
5 7067 0.738407 0.565504 0.009078
6 7246 0.974299 0.723805 0.010159
7 7319 1.050713 0.763544 0.010557
8 7423 1.259032 0.925457 0.012777
9 7540 1.443750 0.955906 0.013920
10 7562 1.482861 1.009565 0.014417
Tab.3  Training sample data set V
No. Time/h Input Output y
x1 x2 x3
1 6977 0.248749 0.244018 0.006548 427.27
2 7014 0.517667 0.448366 0.007854 388.15
3 7198 0.877194 0.704297 0.009541 347.47
4 7356 1.097318 0.838116 0.011837 287.90
5 7575 1.548504 1.012346 0.014772 216.09
Tab.4  Test sample data set T
No. Time/h Actual cost/CNY LS-SVR cost/CNY Relative error (LS-SVR) BP neural network cost/CNY Relative error (BP)
1 6977 427.27 437.10 2.3% 451.20 5.6%
2 7014 388.15 381.94 1.6% 406.78 4.8%
3 7198 347.47 350.94 1.0% 368.67 6.1%
4 7356 287.90 295.67 2.7% 299.13 3.9%
5 7575 216.09 214.15 0.9% 227.11 5.1%
Tab.5  Relative error between LS-SVR predicted and actual values
t/month Point (z1(t), z2(t))
43.2 A (0.0044, 0.3398)
43.4 B (0.0239, 0.3115)
43.6 C (0.0407, 0.2928)
43.8 D (0.0571, 0.2732)
44.0 E (0.0835, 0.2362)
… …
48.2 Z (0.0326, 0.2653)
Tab.6  Design satisfaction with different usage times
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