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Neural Approaches to Dynamics of Signal Exchanges pp 235–244Cite as

Growing Curvilinear Component Analysis (GCCA) for Stator Fault Detection in Induction Machines

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Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 151))

Abstract

Fault diagnostics for electrical machines is a very difficult task because of the non-stationarity of the input information. Also, it is mandatory to recognize the pre-fault condition in order not to damage the machine. Only techniques like the principal component analysis (PCA) and its neural variants are used at this purpose, because of their simplicity and speed. However, they are limited by the fact they are linear. The GCCA neural network addresses this problem; it is nonlinear, incremental, and performs simultaneously the data quantization and projection by using the curvilinear component analysis (CCA), a distance-preserving reduction technique. Using bridges and seeds, it is able to fast adapt and track changes in the data distribution. Analyzing bridge length and density, it is able to detect a pre-fault condition. This paper presents an application of GCCA to a real induction machine on which a time-evolving stator fault in one phase is simulated.

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Acknowledgements

This work has been partly supported by OPLON Italian MIUR project.

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Authors and Affiliations

  1. University of Picardie Jules Verne, Lab. LTI, Amiens, France

    Giansalvo Cirrincione

  2. University of South Pacific, SEP, Suva, Fiji Islands

    Giansalvo Cirrincione, Rahul R. Kumar & Maurizio Cirrincione

  3. Politecnico di Torino, DET, Turin, Italy

    Vincenzo Randazzo & Eros Pasero

Authors
  1. Giansalvo Cirrincione
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  2. Vincenzo Randazzo
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  3. Rahul R. Kumar
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  4. Maurizio Cirrincione
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  5. Eros Pasero
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Corresponding author

Correspondence to Vincenzo Randazzo .

Editor information

Editors and Affiliations

  1. Department of Psychology, University of Campania Luigi Vanvitelli, Caserta, Italy

    Anna Esposito

  2. Tecnocampus, Mataró, Spain

    Marcos Faundez-Zanuy

  3. Department of Civil, Environment, Energy and Materials Engineering, Mediterranea University of Reggio Calabria, Reggio Calabria, Italy

    Francesco Carlo Morabito

  4. Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, Turin, Italy

    Eros Pasero

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Cirrincione, G., Randazzo, V., Kumar, R.R., Cirrincione, M., Pasero, E. (2020). Growing Curvilinear Component Analysis (GCCA) for Stator Fault Detection in Induction Machines. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Neural Approaches to Dynamics of Signal Exchanges. Smart Innovation, Systems and Technologies, vol 151. Springer, Singapore. https://doi.org/10.1007/978-981-13-8950-4_22

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