Abstract
In this paper a new Novelty Detection framework is presented, which is created by taking advantage of the Fuzzy Entropy property of Fuzzy Logic systems. The framework’s aim is to create a linguistic-based feedback mechanism for advising the process users (Human-Centric System) on the performance of a complex manufacturing process. The manufacturing process under investigation is the Friction Stir Welding (FSW) process. The presented methodology comprises a data-driven model-based approach, which is the main facet of the framework. The proposed system has a Neural-Fuzzy structure that learns from process data to predict a number of process characteristics. Via the created Novelty Detection framework, we show how we can take advantage of the properties of the system to a) alert the user when a ‘novelty’ (i.e. new condition) appears in the system, and b) to advise the user in how reliable the system’s predictions are when the novelty occurs. The user feedback is provided in linguistic form by taking advantage the inherent features of the Fuzzy Logic–based approach. A number of simulations and experimental results based on a complex manufacturing case-study demonstrate the effectiveness and usefulness of the created Human-Centric System, which could be used as a form of decision support.
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References
Pedrycz, W., Gomide, F.: Introduction. In: Fuzzy Systems Engineering: Toward Human-Centric Computing, pp. 1–26 (2007)
Paiva, R.P., Dourado, A.: Interpretability and learning in neuro-fuzzy systems. Fuzzy Sets Syst. 147(1), 17–38 (2004)
Elangovan, K., Balasubramanian, V., Babu, S.: Predicting tensile strength of friction stir welded AA6061 aluminium alloy joints by a mathematical model. Mater. Des. 30, 188–193 (2009)
Zhang, Q., Mahfouf, M., Panoutsos, G., Beamish, K., Norris, I.: Multiple Characterisation Modelling of Friction Stir Welding Using a Genetic Multi-objective Data-driven Fuzzy Modelling Approach. In: IEEE International Conference on Fuzzy Systems, pp. 2288–2295 (2011)
Panoutsos, G., Mahfouf, M., Beamish, K., Norris, I.: Combining static and temporal process data in the modelling of FSW weld quality and mechanical properties using Computational Intelligence. In: 8th International Symposium on Friction Stir Welding (2010)
Gupta, M.M., Jin, L., Homma, N.: Radial Basis Function Neural Networks. In: Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory, pp. 223–252. John Wiley & Sons, Inc., New York (2003)
Wang, L.X., Mendel, J.M.: Fuzzy basis functions, universal approximation, and orthogonal least-squares learning. IEEE Trans. Neural Netw. 3(5), 807–814 (1992)
Jantzen, J.: Neurofuzzy modelling, Lyngby, Denmark (1998)
Brotherton, T., Johnson, T.: Anomaly detection for advanced military aircraft using neural networks. In: 2001 IEEE Aerospace Conference Proceedings (Cat. No.01TH8542)., pp. 3113–3123 (2001)
Li, Y., Pont, M.J., Barrie Jones, N.: Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where ‘unknown’ faults may occur. Pattern Recognit. Lett. 23, 569–577 (2002)
Surace, C., Worden, K.: A Novelty Detection Method to Diagnose Damage in Structures: An Application to an Offshore Platform. In: Proceedings of the Eight International Offshore and Polar Engineering Conference (1998)
Sohn, H., Worden, K., Farrar, C.R.: Novelty Detection under Changing Environmental Conditions. In: 8th Annual International Symposium on Smart Structures and Materials (2001)
Crook, P.A., Marshland, S., Hayes, G., Nehmzow, U.: A Tale of Two Filters - On-line Novelty Detection. In: Proceedings of the 2002 IEEE International Conference on Robotics & Automation (2002)
Lee, S.-H., Kim, Y.-T., Cheon, S.-P., Kim, S.: Reliable Data Selection with Fuzzy Entropy. In: Wang, L., Jin, Y. (eds.) FSKD 2005. LNCS (LNAI), vol. 3613, pp. 203–212. Springer, Heidelberg (2005)
Chaghooshi, A., Fathi, M., Kashef, M.: Integration of Fuzzy Shannon’s entropy with fuzzy TOPSIS for industrial robotic system section. J. Ind. Eng. Manag. 5(1), 102–114 (2012)
Liangqun, L., Hongbing, J., Xinbo, G.: Maximum entropy fuzzy clustering with application to real-time target tracking. Signal Processing 86, 3432–3447 (2006)
De Luca, A., Termini, S.: A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory. Inf. Control 20, 301–312 (1972)
De Luca, A., Termini, S.: Entropy of L-fuzzy sets. Inf. Control 73, 55–73 (1974)
Shannon, C.E.: A Mathematical Theory of Communication. Bell Syst. Tech. J. 27(3), 379–423 (1948)
Thomas, W., Nicholas, E., Needham, J., Murch, M., Temple-Smith, P., Dawes, C.: Friction-stir butt welding. 9125978.8 (International patent application No. PCT/GB92/02203) (1991)
Thomas, W.M., Woollin, P., Johnson, K.I.: Friction stir welding of steel - a feasibility study. Steel World 4(2), 55–59 (1999)
Panoutsos, G., Mahfouf, M.: A neural-fuzzy modelling framework based on granular computing: Concepts and applications. Fuzzy Sets Syst. 161(21), 2808–2830 (2010)
Gonzalez-Rodriguez, A., Panoutsos, G., Sinclair, K., Mahfouf, M., Beamish, K.: Model-based process monitoring in Friction Stir Welding. In: 9th International Symposium on Friction Stir Welding (2012)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Springer US (1981)
Zhang, Q., Mahfouf, M.: Fuzzy Predictive Modelling Using Hierarchical Clustering and Multi-objective Optimisation for Mechanical Properties of Alloy Steels. In: Proceedings of the 12th IFAC Symposium on Automation in Mining, Mineral and Metal Processing (IFAC MMM 2007), vol. 12(1), pp. 427–432 (2007)
Linkens, D.A., Nyongesa, H.O.: Learning systems in intelligent control: an appraisal of fuzzy, neural and genetic algorithm control applications. IEE Proc. - Control Theory Appl. 143(4), 367–386 (1996)
Billings, S.A., Zheng, G.L.: Radial basis function network configuration using genetic algorithms. Neural Networks 8(6), 877–890 (1995)
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Gonzalez-Rodriguez, A., Panoutsos, G., Mahfouf, M., Beamish, K. (2015). A Novelty Detection Framework Based on Fuzzy Entropy for a Complex Manufacturing Process. In: Filev, D., et al. Intelligent Systems'2014. Advances in Intelligent Systems and Computing, vol 323. Springer, Cham. https://doi.org/10.1007/978-3-319-11310-4_39
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DOI: https://doi.org/10.1007/978-3-319-11310-4_39
Publisher Name: Springer, Cham
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