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An Online RFID Localization in the Manufacturing Shopfloor

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Predictive Maintenance in Dynamic Systems

Abstract

Radio Frequency Identification technology has gained popularity for cheap and easy deployment. In the realm of manufacturing shopfloor, it can be used to track the location of manufacturing objects to achieve better efficiency. The underlying challenge of localization lies in the nonstationary characteristics of manufacturing shopfloor which calls for an adaptive life-long learning strategy in order to arrive at accurate localization results. This paper presents an evolving model based on a novel evolving intelligent system, namely evolving Type-2 Quantum Fuzzy Neural Network (eT2QFNN), which features an interval type-2 quantum fuzzy set with uncertain jump positions. The quantum fuzzy set possesses a graded membership degree which enables better identification of overlaps between classes. The eT2QFNN works fully in the evolving mode, where all parameters including the number of rules are automatically adjusted and generated on the fly. The parameter adjustment scenario relies on decoupled extended Kalman filter method. Our numerical study shows that eT2QFNN is able to deliver comparable accuracy compared to state-of-the-art algorithms.

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Acknowledgements

This project is fully supported by NTU start-up grant and Ministry of Education Tier 1 Research Grant. We also would like to thank Singapore Institute of Manufacturing Technology which provided the RFID data that greatly assisted the research. The third author acknowledges the support by the LCM–K2 Center within the framework of the Austrian COMET-K2 program.

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Authors and Affiliations

  1. Nanyang Technological University, Singapore, Singapore

    Andri Ashfahani, Mahardhika Pratama & Qing Cai

  2. Fuzzy Logic Laboratorium Linz-Hagenberg, Department of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz, Linz, Austria

    Edwin Lughofer

  3. Singapore Institute of Manufacturing Technology, Singapore, Singapore

    Huang Sheng

Authors
  1. Andri Ashfahani
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  2. Mahardhika Pratama
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  3. Edwin Lughofer
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  4. Qing Cai
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  5. Huang Sheng
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Corresponding author

Correspondence to Andri Ashfahani .

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Editors and Affiliations

  1. Fuzzy Logic Laboratorium Linz-Hagenberg, Department of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz, Linz, Austria

    Edwin Lughofer

  2. Institute Mines-Telecom Lille Douai, Douai, France

    Moamar Sayed-Mouchaweh

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Ashfahani, A., Pratama, M., Lughofer, E., Cai, Q., Sheng, H. (2019). An Online RFID Localization in the Manufacturing Shopfloor. In: Lughofer, E., Sayed-Mouchaweh, M. (eds) Predictive Maintenance in Dynamic Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-05645-2_10

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  • DOI: https://doi.org/10.1007/978-3-030-05645-2_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05644-5

  • Online ISBN: 978-3-030-05645-2

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