Using Deep Neural Networks to Evaluate the System Reliability of Manufacturing Networks

doi:10.23940/ijpe.21.07.p4.600608

Abstract

Abstract: This paper focuses on the system reliability evaluation for a stochastic-flow manufacturing network by a Deep Learning approach. Knowing the capability of the manufacturing system in real time is a critical issue because the manufacturing industry conducts mass production through automated machines. In existing algorithms, system reliability cannot be calculated in a short time when the network model is complex. Hence, an efficient algorithm based on the Deep Neural Network is developed to predict the system reliability instantly. According to the experimental results, the proposed algorithm can predict system reliability with a Root-Mean-Square Error of 0.002. Compared with existing algorithms, the proposed algorithm can evaluate the reliability of a system in only one-tenth of the time.

Key words: manufacturing network, system reliability, deep learning, deep neural network

Yi-Fan Chen, Yi-Kuei Lin, and Cheng-Fu Huang. Using Deep Neural Networks to Evaluate the System Reliability of Manufacturing Networks [J]. Int J Performability Eng, 2021, 17(7): 600-608.

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References

1. Dalenogare L.S., Benitez G.B., Ayala N.F. and Frank A.G.The expected contribution of Industry 4.0 technologies for industrial performance.International Journal of Production Economics, 204, pp.383-394, 2018.
2. Laney D.3D data management: Controlling data volume, velocity and variety. META group research note, 6(70), p.1, 2001.
3. Lin, Y.K. and Chang, P.C.Evaluate the system reliability for a manufacturing network with reworking actions.Reliability Engineering & System Safety, 106, pp.127-137, 2012.
4. Lin Y.K., Huang C.F., Liao Y.C. and Yeh C.C.System reliability for a multistate intermodal logistics network with time windows. International Journal of Production Research,55(7), pp.1957-1969, 2017.
5. Huang, D.H., Huang, C.F. and Lin, Y.K.Exact project reliability for a multi-state project network subject to time and budget constraints. Reliability Engineering & System Safety, 195, p.106744, 2020.
6. Lin Y.K.System reliability evaluation for a multistate supply chain network with failure nodes using minimal paths. IEEE Transactions on Reliability,58(1), pp.34-40, 2009.
7. Niu, Y.F., Gao, Z.Y. and Lam, W.H.A new efficient algorithm for finding all d-minimal cuts in multi-state networks.Reliability Engineering & System Safety, 166, pp.151-163, 2017.
8. Bai, G., Tian, Z. and Zuo, M.J.Reliability evaluation of multistate networks: An improved algorithm using state-space decomposition and experimental comparison. IISE Transactions,50(5), pp.407-418, 2018.
9. Jane, C.C. and Laih, Y.W.Distribution and reliability evaluation of max-flow in dynamic multi-state flow networks. European Journal of Operational Research,259(3), pp.1045-1053, 2017.
10. Chen, S.G. and Lin, Y.K.Search for all minimal paths in a general large flow network. IEEE Transactions on Reliability,61(4), pp.949-956, 2012.
11. Lin, Y.K. and Chen, S.G.A merge search approach to find minimal path vectors in multistate networks. International Journal of Reliability, Quality and Safety Engineering, 24(01), p.1750005, 2017.
12. Lin Y.K.On reliability evaluation of a stochastic-flow network in terms of minimal cuts. Journal of the Chinese institute of industrial engineers,18(3), pp.49-54, 2001.
13. Lin Y.K.Study on the performance index for a multicommodity stochastic-flow network in terms of minimal cuts. Journal of the Chinese Institute of Industrial Engineers,19(3), pp.42-48, 2002.
14. Da, G., Xu, M. and Chan, P.S.An efficient algorithm for computing the signatures of systems with exchangeable components and applications. IISE Transactions,50(7), pp.584-595, 2018.
15. Lin, J.S., Jane, C.C. and Yuan, J.On reliability evaluation of a capacitated‐flow network in terms of minimal pathsets. Networks,25(3), pp.131-138, 1995.
16. Jane, C.C., Lin, J.S. and Yuan, J.Reliability evaluation of a limited-flow network in terms of minimal cutsets. IEEE transactions on reliability,42(3), pp.354-361, 1993.
17. Bai, G., Zuo, M.J. and Tian, Z.Ordering heuristics for reliability evaluation of multistate networks. IEEE Transactions on Reliability,64(3), pp.1015-1023, 2015.
18. Yarlagadda, R.A.O. and Hershey, J.O.H.N., 1991. Fast algorithm for computing the reliability of a communication network. International Journal of Electronics Theoretical and Experimental,70(3), pp.549-564.
19. Aven T.Reliability evaluation of multistate systems with multistate components. IEEE Transactions on reliability,34(5), pp.473-479, 1985.
20. Provan, J.S. and Ball, M.O.The complexity of counting cuts and of computing the probability that a graph is connected. SIAM Journal on Computing,12(4), pp.777-788, 1983.
21. Ball, M. and Van Slyke, R.M. Backtracking algorithms for network reliability analysis. In Annals of discrete Mathematics (Vol. 1, pp. 49-64). Elsevier, 1977.
22. Zheng S., Jayasumana S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C. and Torr, P.H. Conditional random fields as recurrent neural networks. InProceedings of the IEEE international conference on computer vision (pp. 1529-1537), 2015.
23. Lin Y.K.A simple algorithm for reliability evaluation of a stochastic-flow network with node failure. Computers & Operations Research,28(13), pp.1277-1285, 2001.
24. Lin, Y.K. and Chang, P.C.Reliability evaluation for a manufacturing network with multiple production lines. Computers & Industrial Engineering,63(4), pp.1209-1219, 2012.

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