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Gear and bearing fault classification under different load and speed by using Poincaré plot features and SVM

Author

Listed:
  • Rubén Medina

    (Universidad de Los Andes)

  • Jean Carlo Macancela

    (Universidad Politécnica Salesiana)

  • Pablo Lucero

    (Universidad Politécnica Salesiana)

  • Diego Cabrera

    (Universidad Politécnica Salesiana)

  • René-Vinicio Sánchez

    (Universidad Politécnica Salesiana)

  • Mariela Cerrada

    (Universidad Politécnica Salesiana)

Abstract

This paper describes two algorithms for feature extraction from the Poincaré plot which is constructed with the vibration signals measured in roller bearings and gearboxes. The extracted features are used for classifying 10 types of fault conditions in a gearbox and 7 types of fault conditions a roller bearings. Both vibration signal datasets were acquired at different loads and speeds. The feature extraction using Algorithm 1 performs the feature calculation from the Poincaré plot constructed with the raw vibration signals. In contrast, the Algorithm 2 requires an additional stage where the vibration signal is pre-processed for identifying the peaks of the signal. This peak sequence is equivalent to a non-uniform sub-sampling of the vibration signal that retains relevant information useful for fault classification. The fault classification is attained by using a multi-class Support Vector Machine. The proposed method is tested using the tenfold cross-validation. Results show that both algorithms could attain classification accuracies as high as 99.3% for the gearbox dataset and 100% for the roller bearings. The results are compared to other classification approaches performed on the same datasets by using other different features. The comparison shows that the approach in this paper has a performance as good as obtained by using well-known statistical features.

Suggested Citation

  • Rubén Medina & Jean Carlo Macancela & Pablo Lucero & Diego Cabrera & René-Vinicio Sánchez & Mariela Cerrada, 2022. "Gear and bearing fault classification under different load and speed by using Poincaré plot features and SVM," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1031-1055, April.
  • Handle: RePEc:spr:joinma:v:33:y:2022:i:4:d:10.1007_s10845-020-01712-9
    DOI: 10.1007/s10845-020-01712-9
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    References listed on IDEAS

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    1. Qiang Zhou & Ping Yan & Huayi Liu & Yang Xin, 2019. "A hybrid fault diagnosis method for mechanical components based on ontology and signal analysis," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1693-1715, April.
    2. Timo Oertzen & Steven Boker, 2010. "Time Delay Embedding Increases Estimation Precision of Models of Intraindividual Variability," Psychometrika, Springer;The Psychometric Society, vol. 75(1), pages 158-175, March.
    3. Deepam Goyal & Anurag Choudhary & B. S. Pabla & S. S. Dhami, 2020. "Support vector machines based non-contact fault diagnosis system for bearings," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1275-1289, June.
    4. Igba, Joel & Alemzadeh, Kazem & Durugbo, Christopher & Eiriksson, Egill Thor, 2016. "Analysing RMS and peak values of vibration signals for condition monitoring of wind turbine gearboxes," Renewable Energy, Elsevier, vol. 91(C), pages 90-106.
    5. Cong Wang & Meng Gan & Chang’an Zhu, 2017. "Intelligent fault diagnosis of rolling element bearings using sparse wavelet energy based on overcomplete DWT and basis pursuit," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1377-1391, August.
    6. Wentao Huang & Fanzhao Kong & Xuezeng Zhao, 2018. "Spur bevel gearbox fault diagnosis using wavelet packet transform and rough set theory," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1257-1271, August.
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    Cited by:

    1. Changyuan Yang & Sai Ma & Qinkai Han, 2023. "Unified discriminant manifold learning for rotating machinery fault diagnosis," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3483-3494, December.

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