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Diversity Measures in Classifier Ensembles Used for Rotating Machinery Fault Diagnosis

  • Conference paper
  • First Online:
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

Recent progress in computational intelligence, sensor technology and soft computing methods permit the use of complex systems to achieve diagnostic process goal. Among many, machine learning and pattern recognition techniques are often applied. When dealing with complex machinery use of one classifier is often insufficient. It is known that classifier ensembles (combined prediction from several classifiers) have the capability to outperform single classifier, because ensemble results are less dependent on peculiarities of a single training set. Additionally a combination of multiple classifiers may learn a more expressive class. In the paper a comparative study of different diversity measures for the rotating machine common faults detection and isolation. The main premise was to investigate if there is a link between diversity measure and classification accuracy. Although in several cases the connection between diversity and fault detection as well as isolation performance was revealed, the generalization of the diversity measuring concept cannot be clearly formulated.

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Notes

  1. 1.

    The arrow specifies whether greater diversity is reflected in greater \(( \uparrow )\) or lower \(( \downarrow )\) diversity measure value.

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Acknowledgments

Scientific work financed from resources assigned to statutory activity of Institute of Fundamentals of Machinery Design, Silesian University of Technology at Gliwice.

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Authors and Affiliations

  1. Institute of Fundamentals of Machine Design, Silesian University of Technology, 18A Konarskiego St, 44-100, Gliwice, Poland

    Wojciech Jamrozik

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  1. Wojciech Jamrozik
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Correspondence to Wojciech Jamrozik .

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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Jamrozik, W. (2016). Diversity Measures in Classifier Ensembles Used for Rotating Machinery Fault Diagnosis. 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_23

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  • DOI: https://doi.org/10.1007/978-3-319-20463-5_23

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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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