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Research Papers

Prediction of Passive Maintenance Opportunity Windows on Bottleneck Machines in Complex Manufacturing Systems

[+] Author and Article Information
Xi Gu

Department of Mechanical Engineering,
University of Michigan,
2350 Hayward Street,
Ann Arbor, MI 48109
e-mail: xig@umich.edu

Xiaoning Jin

Department of Mechanical Engineering,
University of Michigan,
2350 Hayward Street,
Ann Arbor, MI 48109
e-mail: xnjin@umich.edu

Jun Ni

Fellow ASME
Department of Mechanical Engineering,
University of Michigan,
2350 Hayward Street,
Ann Arbor, MI 48109
e-mail: junni@umich.edu

1Corresponding author.

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received September 30, 2013; final manuscript received February 19, 2015; published online March 12, 2015. Assoc. Editor: Jaime Camelio.

J. Manuf. Sci. Eng 137(3), 031017 (Jun 01, 2015) (9 pages) Paper No: MANU-13-1358; doi: 10.1115/1.4029906 History: Received September 30, 2013; Revised February 19, 2015; Online March 12, 2015

In this paper, we investigate hidden opportunities for performing proper maintenance tasks during production time without causing production losses. One of the maintenance opportunities on a machine is when the machine is starved or blocked due to the occurrence of random failures on its upstream or downstream machines. Such failure-induced starvation or blockage time is defined as a passive maintenance opportunity window (PMOW), and is predicted on the bottleneck machines in manufacturing systems with different configurations. The effectiveness of the PMOW prediction algorithm is validated through case studies in both simulations and an automotive assembly plant.

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References

Mobley, R. K., 2002, An Introduction to Predictive Maintenance, Butterworth-Heinemann, Wobrun, MA.
Koren, Y., 2010, The Global Manufacturing Revolution: Product-Process-Business Integration and Reconfigurable Systems, Wiley, Hoboken, NJ.
Li, J., Blumenfeld, D. E., Huang, N., and Alden, J. M., 2009, “Throughput Analysis of Production Systems: Recent Advances and Future Topics,” Int. J. Prod. Res., 47(14), pp. 3823–3851. [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]
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]
Dallery, Y., and Gershwin, S. B., 1992, “Manufacturing Flow Line Systems: A Review of Models and Analytical Results,” Queueing Syst., 12(1), pp. 3–94. [CrossRef]
Cho, D. I., and Parlar, M., 1991, “A Survey of Maintenance Models for Multi-Unit Systems,” Eur. J. Oper. Res., 51(1), pp. 1–23. [CrossRef]
Nicolai, R. P., and Dekker, R., 2008, “Optimal Maintenance of Multi-Component Systems: A Review,” Complex System Maintenance Handbook, Springer, London, UK.
Wang, H., 2002, “A Survey of Maintenance Policies of Deteriorating Systems,” Eur. J. Oper. Res., 139(3), pp. 469–489. [CrossRef]
Ambani, S., Li, L., and Ni, J., 2009, “Condition-Based Maintenance Decision-Making for Multiple Machine Systems,” ASME J. Manuf. Sci. Eng., 131(3), p. 031009. [CrossRef]
Lee, S., Li, L., and Ni, J., 2013, “Markov-Based Maintenance Planning Considering Repair Time and Periodic Inspection,” ASME J. Manuf. Sci. Eng., 135(3), p. 031013. [CrossRef]
Armbruster, D., Göttlich, S., and Herty, M., 2011, “A Scalar Conservation Law With Discontinuous Flux for Supply Chains With Finite Buffers,” SIAM J. Appl. Math., 71(4), pp. 1070–1087. [CrossRef]
Ni, J., and Jin, X., 2012, “Decision Support Systems for Effective Maintenance Operations,” CIRP Ann. Manuf. Technol., 61(1), pp. 411–414. [CrossRef]
Chang, Q., Biller, S., and Xiao, G., 2010, “Transient Analysis of Downtimes and Bottleneck Dynamics in Serial Manufacturing Systems,” ASME J. Manuf. Sci. Eng., 132(5), p. 051015. [CrossRef]
Liu, J., Chang, Q., Xiao, G., and Biller, S., 2012, “The Costs of Downtime Incidents in Serial Multistage Manufacturing Systems,” ASME J. Manuf. Sci. Eng., 134(2), p. 021016. [CrossRef]
Li, Y., Chang, Q., Brundage, M. P., Xiao, G., and Biller, S., 2013, “Standalone Throughput Analysis on the Wave Propagation of Disturbances in Production Sub-Systems,” ASME J. Manuf. Sci. Eng., 135(5), p. 051001. [CrossRef]
Kuo, C. T., Lim, J. T., and Meerkov, S., 1996, “Bottlenecks in Serial Production Lines: A System-Theoretic Approach,” Math. Probl. Eng., 2(3), pp. 233–276. [CrossRef]
Li, L., Chang, Q., and Ni, J., 2009, “Data Driven Bottleneck Detection of Manufacturing Systems,” Int. J. Prod. Res., 47(18), pp. 5019–5036. [CrossRef]
Gu, X., Lee, S., Liang, X., Garcellano, M., Diederichs, M., and Ni, J., 2013, “Hidden Maintenance Opportunities in Discrete and Complex Production Lines,” Expert Syst. Appl., 40(11), pp. 4353–4361. [CrossRef]
Li, J., and Meerkov, S. M., 2008, Production System Engineering, WingSpan Press, Livermore, CA.
Hopp, W. J., and Spearman, M. L., 2008, Factory Physics, McGraw-Hill, New York.

Figures

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Fig. 1

An N-machine-(N − 1)-buffer serial line

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Fig. 2

An EPSL for the downstream machine MN

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Fig. 3

An example of a complex manufacturing system

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Fig. 4

The state of the bottleneck machine in lines li,(1) and li,(2) when these lines are considered independently (a) and jointly (b)

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Fig. 5

Prediction of PMOW under multiple failures in a complex system

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Fig. 6

Flowchart for PMOW prediction under multiple failures in a complex system

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Fig. 7

System layout for case study 1

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Fig. 8

Decomposition of system into equivalent serial lines l2,1 (a) and l2,1 (b)

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Fig. 9

PMOW prediction for case study 1

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Fig. 10

WIP of the bottleneck machine M6 over time

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Fig. 11

System layout (a) and its model (b) for case study 2

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Fig. 12

PMOW prediction and validation using real-time data from FIS

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