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Knife Diagnostics with Empirical Mode Decomposition

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Advances in Condition Monitoring of Machinery in Non-Stationary Operations (CMMNO 2014)

Part of the book series: Applied Condition Monitoring ((ACM,volume 4))

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Abstract

This paper deals with the condition monitoring of knives via the Empirical Mode Decomposition (EMD). The cutting process is basically transient, thus Fourier Analysis and similar signal processing tools aren’t optimal because they treat signals as they were periodic. EMD is a signal analysis technique which is particularly suited for non-stationary and/or non-linear data, since it adaptively decomposes the signal in a sum of Intrinsic Mode Functions (IMFs). The knives under analysis are used inside an automated packaging machine; they are hydraulically actuated and are mounted on a moving support, so it’s not possible to put sensors on them because of security reasons related to sensors wiring. Instead, the actuators control valve is hosted on a fixed machine part, so its pressure signal is the one analysed in this paper. The sum of two IMFs is used to estimate the knife state and to obtain a representation of the wearing process during a knife life.

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References

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Author information

Authors and Affiliations

  1. Department of Science and Engineering Methods, University of Modena and Reggio Emilia, Via Amendola 2—Morselli Building, 42122, Reggio Emilia, Italy

    Michele Cotogno, Marco Cocconcelli & Riccardo Rubini

Authors
  1. Michele Cotogno
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  2. Marco Cocconcelli
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  3. Riccardo Rubini
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Corresponding author

Correspondence to Michele Cotogno .

Editor information

Editors and Affiliations

  1. National School of Engineers of Sfa, Sfax, Tunisia

    Fakher Chaari

  2. Diagnostics and Vibro-Acoustics Science Laboratory, Wrocław University of Technology Institute of Mining Engineering, Wroclaw, Poland

    Radoslaw Zimroz

  3. Diagnostics and Vibro-Acoustics Science Laboratory, Wrocław Univ of Technology Inst of Mining Engineering, Wroclaw, Poland

    Walter Bartelmus

  4. Mechanical Engineering Department, National School of Engineers of Sfa, Sfax, Tunisia

    Mohamed Haddar

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Cotogno, M., Cocconcelli, M., Rubini, R. (2016). Knife Diagnostics with Empirical Mode Decomposition. In: Chaari, F., Zimroz, R., Bartelmus, W., Haddar, M. (eds) Advances in Condition Monitoring of Machinery in Non-Stationary Operations. CMMNO 2014. Applied Condition Monitoring, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-20463-5_13

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  • DOI: https://doi.org/10.1007/978-3-319-20463-5_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20462-8

  • Online ISBN: 978-3-319-20463-5

  • eBook Packages: EngineeringEngineering (R0)

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