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Reliability evaluation and component importance measure for manufacturing systems based on failure losses

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Abstract

Little work has been done to assess the reliability of a vital system like the manufacturing system. In this article, a novel and effective system reliability evaluation method in terms of failure losses has been proposed for manufacturing systems of job shop type, and then the failure losses based component importance measure (CIM) is used for importance analysis of equipment. The former indicates the present system reliability situation and the latter points the way to reliability improvement efforts. In this scheme, the problem is described and modeled by a dynamic directed network. Consider that the actual processing time of machines is to contribute to failure occurrence, it is used to calculate the failure times and failure losses. The obtained total failure times and failure losses of the system are applied to evaluate its reliability. Techniques to estimate two kinds of failure losses based CIMs are presented. They offer guidelines to realize system reliability growth cost-effectively. A case study of a real job shop is provided as an example to demonstrate the validity of the proposed methods. Comparison to other commonly used methods shows the efficiency of the proposed methods.

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Abbreviations

CIM:

Component importance measure

CNC:

Computer numerical control

DNC:

Distributed numerical control

ERP:

Enterprise resource planning

FT:

Failure times

MES:

Manufacturing execution system

MPT:

Machine processing time

MTBF:

Mean time between failure

MTTF:

Mean time to failure

MTTR:

Mean time to repair

OEE:

Overall equipment effectiveness

RFID:

Radio frequency identification

WDN:

Weighted and directed network

WIP:

Work-in-process

\(t, T\) :

Sequence number of time intervals (number of all intervals is \(T\))

\(i, I_{t}\) :

Sequence number of products items (number of all items is \(I_{t}\))

\(j, J_{t}\) :

Sequence number of processes during \(t\) (\(J_{t}\) is the maximum value)

\(k, K\) :

Sequence number of equipment (number of all equipment is \(K\))

\(G_{t}\) :

WDN during \(t\)

\(M_{k}\) :

Equipment \(k\)

\(r_{k}\) :

Failure rate of \(M_{k}\)

\(FT_{tk}\) :

Failure times of \(M_{k}\) during \(t\)

\(T_{total}\) :

Total time

\(V_{ti}\) :

Volume size of item \(i\) during \(t\)

\(B_{tij}\) :

Processing time of item \(i\) for the \(j\hbox {th}\) process during \(t\)

\(x_{tijk}\) :

Be equal to 1 if \(M_{k}\) is used for machining the \(j\hbox {th}\) process of item \(i\) during \(t\), 0 otherwise

\(MTBF_{k}\) :

MTBF of \(M_{k}\)

\(MTTR_{k}\) :

MTTR of \(M_{k}\)

\(C_{k}\) :

Average maintenance cost for every repair process of \(M_{k}\)

\(PL_{tk}\) :

Production losses for \(M_{k}\) during time period \(t\)

\(C_{k}^{f}\) :

Fixed cost for spare parts or components

\(C_{k}^{v}\) :

Variable cost for labor cost

\(c\) :

Unit labor cost of maintenance crews

\(U_{tijk}\) :

Occupancy rate of \(M_{k}\) processing the \(j\hbox {th}\) process of Item \(i\) during \(t\)

\(s_{i}\) :

Profit for every item \(i\)

\(FT_{total}\) :

Total failure times

\(FL_{total}\) :

Total failure losses

\(\overline{FL}\) :

Average losses of every failure

\(I_{k-MTBF}^{FL}\) :

CIM in terms of \(MTBF_{k}\)

\(I_{k-MTTR}^{FL}\) :

CIM in terms of \(MTTR_{k}\)

References

  • Abd-El-Barr, M. (2009). Topological network design: A survey. Journal of Network and Computer Applications, 32(3), 501–509.

    Article  Google Scholar 

  • Barker, K., Ramirez-Marquez, J. E., & Rocco, C. M. (2013). Resilience-based network component importance measures. Reliability Engineering and System Safety, 117, 89–97.

    Article  Google Scholar 

  • Birnbaum, Z. W. (1969). On the importance of different components in a multicomponent system. In P. R. Krishnaiah (Ed.), Multivariate analysis (pp. 581–592). New York: Academic Press.

    Google Scholar 

  • Borangiu, T., Raileanu, S., Trentesaux, D., Berger, T., & Iacob, I. (2014). Distributed manufacturing control with extended CNP interaction of intelligent products. Journal of Intelligent Manufacturing, 25(5), 1065–1075.

    Article  Google Scholar 

  • Carlier, J., Li, Y., & Lutton, J.-L. (1997). Reliability evaluation of large telecommunication networks. Discrete Applied Mathematics, 76(1–3), 61–80.

    Article  Google Scholar 

  • Chew, S. F., Wang, S., & Lawley, M. A. (2011). Resource failure and blockage control for production systems. International Journal of Computer Integrated Manufacturing, 24(3), 229–241.

    Article  Google Scholar 

  • Chryssolouris, G. (2005). Manufacturing systems: theory and practice (2nd ed.). New York: Springer.

    Google Scholar 

  • Colledani, M., Tolio, T., Fischer, A., Iung, B., Lanza, G., Schmitt, R., et al. (2014). Design and management of manufacturing systems for production quality. CIRP Annals-Manufacturing Technology, 63(2), 773–796.

    Article  Google Scholar 

  • Dai, Y., & Jia, Y. (2001). Reliability of a VMC and its improvement. Reliability Engineering and System Safety, 72(1), 99–102.

    Article  Google Scholar 

  • De Meo, P., Ferrara, E., Fiumara, G., & Ricciardello, A. (2012). A novel measure of edge centrality in social networks. Knowledge-Based Systems, 30, 136–150.

    Article  Google Scholar 

  • Efthymiou, K., Pagoropoulos, A., Papakostas, N., Mourtzis, D., & Chryssolouris, G. (2014). Manufacturing systems complexity: An assessment of manufacturing performance indicators unpredictability. CIRP Journal of Manufacturing Science and Technology, 7(4), 324–334.

    Article  Google Scholar 

  • ElMaraghy, W., ElMaraghy, H., Tomiyama, T., & Monostori, L. (2012). Complexity in engineering design and manufacturing. CIRP Annals-Manufacturing Technology, 61(2), 793–814.

    Article  Google Scholar 

  • Hiremath, N. C., Sahu, S., & Tiwari, M. (2013). Multi objective outbound logistics network design for a manufacturing supply chain. Journal of Intelligent Manufacturing, 24(6), 1071–1084.

    Article  Google Scholar 

  • Jiang, P. Y., Zhou, G. H., Zhao, G., Zhang, Y. F., & Sun, H. B. (2007). e2-MES: An e-service-driven networked manufacturing platform for extended enterprises. International Journal of Computer Integrated Manufacturing, 20(2–3), 127–142.

    Article  Google Scholar 

  • Kuo, W., & Wan, R. (2007). Recent advances in optimal reliability allocation. IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans, 37(2), 143–156.

    Article  Google Scholar 

  • Lin, Y.-K., & Chang, P.-C. (2013). Reliability-based performance indicator for a manufacturing network with multiple production lines in parallel. Journal of Manufacturing Systems, 32(1), 147–153.

    Article  Google Scholar 

  • Liu, F., Zhu, H., Shao, X., & Gao, G. (2011). Analysis of horizontal machining center field failure data based on generalized linear mixed model—A case study. Quality and Reliability Engineering International, 27(2), 239–248.

    Article  Google Scholar 

  • Mariano, C., & Kuri-Morales, A. (2014). Complex componential approach for redundancy allocation problem solved by simulation-optimization framework. Journal of Intelligent Manufacturing, 25(4), 661–680.

    Article  Google Scholar 

  • Mourtzis, D., Doukas, M., & Psarommatis, F. (2014). Design of manufacturing networks for mass customisation using an intelligent search method. International Journal of Computer Integrated Manufacturing, 1–22, doi:10.1080/0951192x.2014.900867.

  • Mourtzis, D., & Doukas, M. (2014). The evolution of manufacturing systems: from craftsmanship to the era of customization, Design and Management of Lean Production Systems (Modrak, V., & Semanco (P ed.). Pennsylvania: IGI Global.

    Google Scholar 

  • Muchiri, P., & Pintelon, L. (2008). Performance measurement using overall equipment effectiveness (OEE): Literature review and practical application discussion. International Journal of Production Research, 46(13), 3517–3535.

    Article  Google Scholar 

  • Papakostas, N., Efthymiou, K., Georgoulias, K., & Chryssolouris, G. (2012). On the configuration and planning of dynamic manufacturing networks. Logistics Research, 5(3–4), 105–111.

    Article  Google Scholar 

  • Qu, T., Yang, H. D., Huang, G., Zhang, Y. F., Luo, H., & Qin, W. (2012). A case of implementing RFID-based real-time shop-floor material management for household electrical appliance manufacturers. Journal of Intelligent Manufacturing, 23(6), 2343–2356.

    Article  Google Scholar 

  • Rocco, S. C. M., & Ramirez-Marquez, J. E. (2012). Innovative approaches for addressing old challenges in component importance measures. Reliability Engineering and System Safety, 108, 123–130.

  • Sadjadi, S. J., & Soltani, R. (2009). An efficient heuristic versus a robust hybrid meta-heuristic for general framework of serial-parallel redundancy problem. Reliability Engineering and System Safety, 94(11), 1703–1710.

    Article  Google Scholar 

  • Sun, J.-W., Xi, L.-F., Du, S.-C., & Ju, B. (2008). Reliability modeling and analysis of serial-parallel hybrid multi-operational manufacturing system considering dimensional quality, tool degradation and system configuration. International Journal of Production Economics, 114(1), 149–164.

    Article  Google Scholar 

  • Todinov, M. T. (2006a). Reliability analysis based on the losses from failures. Risk Analysis, 26(2), 311–335.

    Article  Google Scholar 

  • Todinov, M. T. (2006b). Reliability value analysis of complex production systems based on the losses from failures. International Journal of Quality Reliability Management, 23(6), 696–718.

    Article  Google Scholar 

  • Tsarouhas, P. H. (2012). Evaluation of overall equipment effectiveness in the beverage industry: A case study. International Journal of Production Research, 51(2), 515–523.

    Article  Google Scholar 

  • Tsarouhas, P. H. (2013). Equipment performance evaluation in a production plant of traditional Italian cheese. International Journal of Production Research, 51(19), 5897–5907.

    Article  Google Scholar 

  • Tsarouhas, P. H., & Arvanitoyannis, I. S. (2010). Assessment of operation management for beer packaging line based on field failure data: A case study. Journal of Food Engineering, 98(1), 51–59.

    Article  Google Scholar 

  • Vrabič, R., Husejnagić, D., & Butala, P. (2012). Discovering autonomous structures within complex networks of work systems. CIRP Annals-Manufacturing Technology, 61(1), 423–426.

    Article  Google Scholar 

  • Vrabič, R., Škulj, G., & Butala, P. (2013). Anomaly detection in shop floor material flow: A network theory approach. CIRP Annals-Manufacturing Technology, 62(1), 487–490.

    Article  Google Scholar 

  • Wang, Y., Deng, C., Wu, J., & Xiong, Y. (2013). Failure time prediction for mechanical device based on the degradation sequence. Journal of Intelligent Manufacturing, 1–19, doi:10.1007/s10845-013-0849-4.

  • Wang, Y., Jia, Y., Yu, J., Zheng, Y., & Yi, S. (1999). Failure probabilistic model of CNC lathes. Reliability Engineering and System Safety, 65(3), 307–314.

    Article  Google Scholar 

  • Watcharasitthiwat, K., & Wardkein, P. (2009). Reliability optimization of topology communication network design using an improved ant colony optimization. Computers and Electrical Engineering, 35(5), 730–747.

    Article  Google Scholar 

  • Wei, T., & Khoshnevis, B. (2000). Integration of process planning and scheduling—A review. Journal of Intelligent Manufacturing, 11(1), 51–63.

    Article  Google Scholar 

  • Yang, Z., Djurdjanovic, D., & Ni, J. (2008). Maintenance scheduling in manufacturing systems based on predicted machine degradation. Journal of Intelligent Manufacturing, 19(1), 87–98.

    Article  Google Scholar 

  • Yingjie, Z., & Liling, G. (2014). Reliability analysis of machining systems by considering system cost. International Journal of Computer Integrated Manufacturing, 1–8, doi:10.1080/0951192x.2014.914630.

  • Zhang, F. Q., Jiang, P. Y., Zheng, M., & Cao, W. (2013). A performance evaluation method for radio frequency identification-based tracking network of job-shop-type work-in-process material flows. Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture, 227(10), 1541–1557.

    Article  Google Scholar 

  • Zio, E., & Piccinelli, R. (2010). Randomized flow model and centrality measure for electrical power transmission network analysis. Reliability Engineering and System Safety, 95(4), 379–385.

    Article  Google Scholar 

Download references

Acknowledgments

The authors are grateful to the Technical Editor and all Reviewers for their valuable and constructive comments. This work was supported by the major national science & technology program (top-grade CNC machine tools and basic manufacturing equipment) under Grant No. 2011ZX04016-101.

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Authors and Affiliations

  1. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, People’s Republic of China

    Ding Zhang, Yingjie Zhang, Mingrang Yu & Yun Chen

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  1. Ding Zhang
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Correspondence to Ding Zhang.

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Zhang, D., Zhang, Y., Yu, M. et al. Reliability evaluation and component importance measure for manufacturing systems based on failure losses. J Intell Manuf 28, 1859–1869 (2017). https://doi.org/10.1007/s10845-015-1073-1

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