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Machine Learning Based Monitoring of the Pneumatic Actuators’ Behavior Through Signal Processing Using Real-World Data Set

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11814))

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Abstract

Application of machine learning in smart manufacturing could enrich condition-based maintenance, enabling to monitor more complex signals describing the behavior of an equipment. Predicting equipment malfunction or failure could not only reduce manufacturing cost but could also improve product quality and supply reliability, aspects equally important these days. This research describes a machine learning based method to diagnose the condition of pneumatic actuators by processing complex signals from this type of equipment. It is using real-world data from a Hungarian multinational manufacturer of electrical components. The “Manufacturing Data Life Cycle” conceptual framework by Tao et al. [1] was followed for data processing, including data cleaning, pre-processing and classification by hierarchical clustering. The method was able to identify those signal patterns that indicate abnormal behavior of the equipment. Data volume was a challenge for this work, overcome by using parallel and GPU execution of computations.

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Acknowledgement

We would like to thank TE Connectivity Hungary to provide data and being able to participate in their research. Project no. NKFIH-869-10/2019 has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the Tématerületi Kiválósági Program funding scheme.

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Authors and Affiliations

  1. Corvinus University of Budapest, Fővám Tér 8, Budapest, Hungary

    Tibor Kovács & Andrea Kő

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  1. Tibor Kovács
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  2. Andrea Kő
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Correspondence to Tibor Kovács .

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Editors and Affiliations

  1. Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler Universität Linz, Linz, Austria

    Josef Küng

  3. Hosei University, Tokyo, Japan

    Makoto Takizawa

  4. Telecommunications University, Nha Trang City, Vietnam

    Son Ha Bui

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Kovács, T., Kő, A. (2019). Machine Learning Based Monitoring of the Pneumatic Actuators’ Behavior Through Signal Processing Using Real-World Data Set. In: Dang, T., Küng, J., Takizawa, M., Bui, S. (eds) Future Data and Security Engineering. FDSE 2019. Lecture Notes in Computer Science(), vol 11814. Springer, Cham. https://doi.org/10.1007/978-3-030-35653-8_3

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  • DOI: https://doi.org/10.1007/978-3-030-35653-8_3

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  • Print ISBN: 978-3-030-35652-1

  • Online ISBN: 978-3-030-35653-8

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