Research Papers

Energy-Oriented Maintenance Decision-Making for Sustainable Manufacturing Based on Energy Saving Window

[+] Author and Article Information
Tangbin Xia

State Key Laboratory of Mechanical
System and Vibration,
Department of Industrial Engineering,
School of Mechanical Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China
e-mail: xtbxtb@sjtu.edu.cn

Lifeng Xi, Shichang Du, Lei Xiao

State Key Laboratory of Mechanical
System and Vibration,
Department of Industrial Engineering,
School of Mechanical Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China

Ershun Pan

State Key Laboratory of Mechanical
System and Vibration,
Department of Industrial Engineering,
School of Mechanical Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China
e-mail: pes@sjtu.edu.cn

1Corresponding author.

Manuscript received June 1, 2017; final manuscript received December 28, 2017; published online February 15, 2018. Assoc. Editor: Karl R. Haapala.

J. Manuf. Sci. Eng 140(5), 051001 (Feb 15, 2018) (12 pages) Paper No: MANU-17-1349; doi: 10.1115/1.4038996 History: Received June 01, 2017; Revised December 28, 2017

In recent years, the industry's responsibility to join in sustainable manufacturing becomes huge, while innovating sustainability has been a new trend. Industrial enterprises are pursuing energy reduction to meet future needs for sustainable globalization and government legislations for green manufacturing. To run a manufacturing line in an energy-efficient manner, an energy-oriented maintenance methodology is developed. At the machine layer, the multi-attribute model (MAM) method is extended by modeling the energy attribute. Preventive maintenance (PM) intervals of each machine are dynamically scheduled according to the machine deterioration, maintenance effects, and environmental conditions. At the system layer, a novel energy saving window (ESW) policy is proposed to reduce energy for the whole line. Energy consumption interactivities, batch production characteristics, and system-layer maintenance opportunities are comprehensively considered. Real-time choice of PM adjustments is scheduled by comparing the energy savings of advanced PM and delayed PM. The results prove the energy reduction achieved by this MAM-ESW methodology. It effectively utilizes standby power, reduces energy consumption, avoids manufacturing breakdown, and decreases scheduling complexity. Furthermore, this energy-oriented maintenance framework can be applied not only in the automotive industry but also for a broader range of manufacturing domains such as the aerospace, semiconductor, and chemical industries.

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Xia, T. , Jin, X. , Xi, L. , and Ni, J. , 2015, “Production-Driven Opportunistic Maintenance for Batch Production Based on MAM-APB Scheduling,” Eur. J. Oper. Res., 240(3), pp. 781–790. [CrossRef]
Jayal, A. D. , Badurdeen, F. , Dillon , O. W., Jr. , and Jawahir, I. S. , 2010, “Sustainable Manufacturing: Modeling and Optimization Challenges at the Product, Process and System Levels,” CIRP J. Manuf. Sci. Technol., 2(3), pp. 144–152. [CrossRef]
Hamzah, S. , and Kobayashi, K. , 2013, “Utilizing Mid-Long Term Maintenance Management Policy for Sustainable Maintenance of Infrastructure Facilities,” Proc. Environ. Sci., 17, pp. 478–484. [CrossRef]
Lee, J. , Kao, H. A. , and Yang, S. , 2014, “Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment,” Proc. CIRP, 16, pp. 3–8. [CrossRef]
Haapala, K. R. , Zhao, F. , Camelio, J. , Sutherland, J. W. , Skerlos, S. J. , Dornfeld, D. A. , Jawahir, I. S. , Clarens, A. F. , and Rickli, J. L. , 2013, “A Review of Engineering Research in Sustainable Manufacturing,” ASME J. Manuf. Sci. Eng., 135(4), p. 041013. [CrossRef]
Zou, J. , Arinez, J. , Chang, Q. , and Lei, Y. , 2016, “Opportunity Window for Energy Saving and Maintenance in Stochastic Production Systems,” ASME J. Manuf. Sci. Eng., 138(12), p. 121009. [CrossRef]
Zhang, H. , Zhao, F. , and Sutherland, J. W. , 2017, “Scheduling of a Single Flow Shop for Minimal Energy Cost Under Real-Time Electricity Pricing,” ASME J. Manuf. Sci. Eng., 139(1), p. 014502. [CrossRef]
Xu, K. , and Tang, K. , 2017, “Optimal Workpiece Setup for Time-Efficient and Energy-Saving Five-Axis Machining of Freeform Surfaces,” ASME J. Manuf. Sci. Eng., 139(5), p. 051003. [CrossRef]
Djurdjanovic, D. , and Ni, J. , 2006, “Stream-of-Variation (SoV)-Based Measurement Scheme Analysis in Multistation Machining Systems,” IEEE T. Autom. Sci. Eng., 3(4), pp. 407–422. [CrossRef]
Zhu, X. , Hu, S. J. , Koren, Y. , and Marin, S. P. , 2008, “Modeling of Manufacturing Complexity in Mixed-Model Assembly Lines,” ASME J. Manuf. Sci. Eng., 130(5), p. 051013. [CrossRef]
Lee, C. W. , 2009, “Dynamic Optimization of the Grinding Process in Batch Production,” ASME J. Manuf. Sci. Eng., 131(2), p. 021006. [CrossRef]
Cardenas-Barron, L. E. , 2009, “On Optimal Batch Sizing in a Multi-Stage Production System With Rework Consideration,” Eur. J. Oper. Res., 196(3), pp. 1238–1244. [CrossRef]
Rafiee, K. , Feng, Q. , and Coit, D. W. , 2014, “Reliability Modeling for Dependent Competing Failure Processes With Changing Degradation Rate,” IIE Trans., 46(5), pp. 483–496. [CrossRef]
Xia, T. , Xi, L. , Pan, E. , Fang, X. , and Gebraeel, N. , 2017, “Lease-Oriented Opportunistic Maintenance for Multi-Unit Leased Systems Under Product-Service Paradigm,” ASME J. Manuf. Sci. Eng., 139(7), p. 071005. [CrossRef]
Tao, X. , Xia, T. , and Xi, L. , 2017, “Dynamic Opportunistic Maintenance Scheduling for Series Systems Based on Theory of Constraints (TOC)–VLLTW Methodology,” ASME J. Manuf. Sci. Eng., 139(2), p. 021009. [CrossRef]
Tian, Z. , 2012, “An Artificial Neural Network Method for Remaining Useful Life Prediction of Equipment Subject to Condition Monitoring,” J. Intell. Manuf., 23(2), pp. 227–237. [CrossRef]
Lee, J. , Wu, F. , Zhao, W. , Ghaffari, M. , Liao, L. , and Siegel, D. , 2014, “Prognostics and Health Management Design for Rotary Machinery Systems—Reviews, Methodology and Applications,” Mech. Syst. Signal Process, 42(1–2), pp. 314–334. [CrossRef]
Xia, T. , Jin, X. , Xi, L. , Zhang, Y. , and Ni, J. , 2015, “Operating Load Based Real-Time Rolling Grey Forecasting for Machine Health Prognosis in Dynamic Maintenance Schedule,” J. Intell. Manuf., 26(2), pp. 269–280. [CrossRef]
Elwany, A. H. , and Gebraeel, N. , 2008, “Sensor-Driven Prognostic Models for Equipment Replacement and Spare Parts Inventory,” IIE Trans., 40(7), pp. 629–639. [CrossRef]
Liao, W. , Pan, E. , and Xi, L. , 2010, “Preventive Maintenance Scheduling for Repairable System With Deterioration,” J. Intell. Manuf., 21(6), pp. 875–884. [CrossRef]
Xia, T. , Xi, L. , Zhou, X. , and Du, S. , 2012, “Modeling and Optimizing Maintenance Schedule for Energy Systems Subject to Degradation,” Comput. Ind. Eng., 63(3), pp. 607–614. [CrossRef]
Zhou, J. , Djurdjanovic, D. , Ivy, J. , and Ni, J. , 2007, “Integrated Reconfiguration and Age-Based Preventive Maintenance Decision Making,” IIE Trans., 39(12), pp. 1085–1102. [CrossRef]
Jin, X. , and Ni, J. , 2013, “Joint Production and Preventive Maintenance Strategy for Manufacturing Systems With Stochastic Demand,” ASME J. Manuf. Sci. Eng., 135(3), p. 031016. [CrossRef]
Celen, M. , and Djurdjanovic, D. , 2015, “Integrated Maintenance Decision-Making and Product Sequencing in Flexible Manufacturing Systems,” ASME J. Manuf. Sci. Eng., 137(4), p. 041006. [CrossRef]
Li, L. , You, M. , and Ni, J. , 2009, “Reliability-Based Dynamic Maintenance Threshold for Failure Prevention of Continuously Monitored Degrading Systems,” ASME J. Manuf. Sci. Eng., 131(3), p. 031010. [CrossRef]
Xia, T. , Xi, L. , Zhou, X. , and Lee, J. , 2012, “Dynamic Maintenance Decision-Making for Series-Parallel Manufacturing System Based on MAM-MTW Methodology,” Eur. J. Oper. Res., 221(1), pp. 231–240. [CrossRef]
Ni, J. , and Jin, X. , 2012, “Decision Support Systems for Effective Maintenance Operations,” CIRP Ann. Manuf. Technol., 61(1), pp. 411–414. [CrossRef]
Gu, X. , Jin, X. , and Ni, J. , 2015, “Prediction of Passive Maintenance Opportunity Windows on Bottleneck Machines in Complex Manufacturing Systems,” ASME J. Manuf. Sci. Eng., 137(3), p. 031017. [CrossRef]
Chang, Q. , Ni, J. , Bandyopadhyay, P. , Biller, S. , and Xiao, G. , 2007, “Maintenance Opportunity Planning System,” ASME J. Manuf. Sci. Eng., 129(3), pp. 661–668. [CrossRef]
Derigent, W. , Thomas, E. , Levrat, E. , and Lung, B. , 2009, “Opportunistic Maintenance Based on Fuzzy Modelling of Component Proximity,” CIRP Ann. Manuf. Technol., 58(1), pp. 29–32. [CrossRef]
Ni, J. , Gu, X. , and Jin, X. , 2015, “Preventive Maintenance Opportunities for Large Production Systems,” CIRP Ann. Manuf. Technol., 64(1), pp. 447–450. [CrossRef]
Sun, Z. , and Li, L. , 2013, “Opportunity Estimation for Real-Time Energy Control of Sustainable Manufacturing Systems,” IEEE Trans. Autom. Sci. Eng., 10(1), pp. 38–44. [CrossRef]
Sari, E. , Shaharoun, A. M. , Ma'aram, A. , and Yazid, A. M. , 2015, “Sustainable Maintenance Performance Measures: A Pilot Survey in Malaysian Automotive Companies,” Proc. CIRP, 26, pp. 443–448. [CrossRef]
Ye, X. , Xia, X. , Zhang, L. , and Zhu, B. , 2015, “Optimal Maintenance Planning for Sustainable Energy Efficiency Lighting Retrofit Projects by a Control System Approach,” Control Eng. Pract., 37(4), pp. 1–10. [CrossRef]
Hoang, A. , Do, P. , and Iung, B. , 2017, “Energy Efficiency Performance-Based Prognostics for Aided Maintenance Decision-Making: Application to a Manufacturing Platform,” J. Clean. Prod., 142(Pt. 4), pp. 2838–2857. [CrossRef]
Xu, W. , and Cao, L. , 2014, “Energy Efficiency Analysis of Machine Tools With Periodic Maintenance,” Int. J. Prod. Res., 52(18), pp. 5273–5285. [CrossRef]
Doyen, L. , and Gaudoin, O. , 2004, “Classes of Imperfect Repair Models Based on Reduction of Failure Intensity or Virtual Age,” Reliab. Eng. Syst. Saf., 84(1), pp. 45–56. [CrossRef]
Pham, H. , and Wang, H. , 1996, “Imperfect Maintenance,” Eur. J. Oper. Res., 94(3), pp. 425–438. [CrossRef]
Cheng, C. , Yang, K. , and Hwang, C. , 1999, “Evaluating Attack Helicopters by AHP Based on Linguistic Variable Weight,” Eur. J. Oper. Res., 116(2), pp. 423–435. [CrossRef]


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Fig. 1

Design of MAM-ESW methodology

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Fig. 2

Illustration of ESW programming

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Fig. 3

Flowchart of energy-oriented maintenance decision-making

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Fig. 4

Maintenance energy comparison with periodic machine-layer models

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Fig. 5

System-layer cumulative energy savings in batch cycles

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Fig. 6

TES comparison with classical policies



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