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2019 | OriginalPaper | Buchkapitel

A Hidden Markov Model Based Approach to Modeling and Monitoring of Processes with Imperfect Maintenance

verfasst von : Deyi Zhang, Dragan Djurdjanović

Erschienen in: Proceedings of the 4th International Conference on the Industry 4.0 Model for Advanced Manufacturing

Verlag: Springer International Publishing

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Abstract

Maintenance interventions are usually imperfect. In this paper, we propose a novel degradation model that addresses the uncertainty in maintenance effectiveness. The new model assumes system’s degradation level at the end of any production run can be recovered to a random degree by the subsequent maintenance activity. Based on parametric uncertainty in the newly proposed model, a novel process monitoring method is proposed for providing condition indicator each time a new observation is retained from the monitored system. Using a large-scale semiconductor dataset, significant improvement in the log-likelihood was observed in the HMM assuming imperfect maintenance against the HMM assuming perfect maintenance. In addition, it is shown that the newly proposed monitoring method is capable of dramatically reducing false alarm ratios, compared to the conventional multivariate signature-based methods.

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These matrices satisfy \( P_{ij}^{(r)} = 0,\,\text{for}\,1 \le j < i \le N,\forall r \).

These matrices satisfy \( P_{ij}^{(\rho )} = 0,\,\text{for}\,1 \le i < j \le N,\forall \rho \).

The parameters of the corresponding degradation HMMs are different for each film thick-ness.

Signals from dozens of sensors were collected during more than 30,000 depositions, and all the signals were collected concurrently at 10 Hz.

Bleakie, A., Djurdjanovic, D.: Feature extraction, condition monitoring, and fault modeling in semiconductor manufacturing systems. Comput. Ind. 64(3), 203–213 (2013)CrossRef

Brown, A.J.J., Catterson, V.M., Fox, M., Long, D., McArthur, S.D.J.: Learning models of plant behavior for anomaly detection and condition monitoring. In: 2007 International Conference on Intelligent Systems Applications to Power Systems, pp. 1–6 (2007)

Celen, M.: Joint maintenance and production operations decision making in flexible manufacturing systems. Ph.D. Thesis, The University of Texas at Austin, Austin, TX (2016)

Cholette, M.E., Djurdjanovic, D.: Degradation modeling and monitoring of machines using operation-specific hidden Markov models. IIE Trans. 46(10), 1107–1123 (2014)CrossRef

Forney, G.D.: The Viterbi algorithm. Proc. IEEE 61(3), 268–278 (1973)MathSciNetCrossRef

Fox, M., Gough, J., Long, D.: Detecting execution failures using learned action models. In: Proceedings of The National Conference on Artificial Intelligence, vol. 22, no. 2, p. 968–973 (2007)

Giantomassi, A., Ferracuti, F., Benini, A., Ippoliti, G., Longhi, S., Petrucci, A.: Hidden Markov model for health estimation and prognosis of turbofan engines. In: International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pp. 681–689 (2011)

Heck, L., McClellan, J.H.: Mechanical system monitoring using hidden Markov models. In: 1991 IEEE International Conference on Acoustics, Speech, and Signal Processing, Toronto, Ontario, Canada, 14–17 April 1991, vol. 3, pp. 1697–1700 (1991)

Hernando, D., Crespi, V., Cybenko, G.: Efficient computation of the hidden Markov model entropy for a given observation sequence. IEEE Trans. Inf. Theory 51(7), 2681–2685 (2005)MathSciNetCrossRef

10.

Kern, W.: The evolution of silicon wafer cleaning technology. J. Electrochem. Soc. 137(6), 1887–1892 (1990)CrossRef

11.

Kohonen, T., Oja, E., Simula, O., Visa, A., Kangas, J.: Engineering applications of the self-organizing map. Proc. IEEE 84(10), 1358–1383 (1996)CrossRef

12.

Lee, J., Ni, J., Djurdjanovic, D., Qiu, H., Liao, H.T.: Intelligent prognostics tools and e-maintenance. Comput. Ind. 57(6), 476–489 (2006)CrossRef

13.

Macgregor, J.F., Kourti, T.: Statistical process-control of multivariate processes. Control Eng. Pract. 3(3), 403–414 (1995)CrossRef

14.

Pham, H., Wang, H.: Imperfect maintenance. Eur. J. Oper. Res. 94(3), 425–438 (1996)CrossRef

15.

Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77, 257–286 (1989)CrossRef

16.

Smyth, P.: Hidden Markov models for fault detection in dynamic systems. Pattern Recogn. 27(1), 149–164 (1994)CrossRef

17.

Tai, A.H., Ching, W.K., Chan, L.Y.: Detection of machine failure: hidden Markov model approach. Comput. Ind. Eng. 57(2), 608–619 (2009)CrossRef

18.

Thomas, H., Morfill, G.E., Demmel, V.V., Goree, J., Feuerbacher, B., Mohlmann, D.: Plasma crystal: coulomb crystallization in a dusty plasma. Phys. Rev. Lett. 73(5), 652–655 (1994)CrossRef

19.

Tobon-Mejia, D.A., Medjaher, K., Zerhouni, N., Tripot, G.: A data-driven failure prognostics method based on mixture of gaussians hidden Markov models. IEEE Trans. Reliab. 61(2), 491–503 (2012)CrossRef

20.

Wang, L., Mehrabi, M.G., Kannatey-Asibu, J.E.: Hidden Markov model-based tool wear monitoring in turning. J. Manuf. Sci. Eng. 124(3), 651–658 (2002)CrossRef

21.

Wong, V., Richardson, B.C., Lui, A., Baldwin, S.: U.S. Patent No. 6,815,362. U.S. Patent and Trademark Office, Washington, DC (2004)

22.

Zhang, D., Bailey, A.D., Djurdjanovic, D.: Bayesian identification of hidden Markov models and their use for condition-based monitoring. IEEE Trans. Reliab. 65(3), 1471–1482 (2016)CrossRef

Titel: A Hidden Markov Model Based Approach to Modeling and Monitoring of Processes with Imperfect Maintenance
verfasst von: Deyi Zhang
Dragan Djurdjanović
Verlag: Springer International Publishing
Buch: Proceedings of the 4th International Conference on the Industry 4.0 Model for Advanced Manufacturing
Print ISBN: 978-3-030-18179-6

Electronic ISBN: 978-3-030-18180-2

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-18180-2_13

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