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Contribution of angular measurements to intelligent gear faults diagnosis

Author

Listed:
  • Semchedine Fedala

    (Setif -1- University)

  • Didier Rémond

    (LaMCoS, INSA-Lyon)

  • Rabah Zegadi

    (Setif -1- University)

  • Ahmed Felkaoui

    (Setif -1- University)

Abstract

Currently, work on the automation of vibration diagnosis is mainly based on indicators extracted from Time sampled Acceleration signals. There are other attractive alternatives such as those based on Angle synchronized measurements, which can provide a considerable number of more relevant and diverse indicators and, thus, lead to better performance in gear fault classification. The diversity of angular measurements (Instantaneous Angular Speed, Transmission Error and Angular sampled Acceleration) represents potential sources of relevant information in fault detection and diagnosis systems. These complementary measurements of existing signals or new relevant signals allow the construction of Feature Vector (FV) offering robust and effective classification methods even for different or non-stationary running speed conditions. In this paper, we propose to build several FVs based on indicators derived from the angular techniques to compare them to the ones calculated from the time signals, proving their superior performance in detection and identification of gear faults. It will be a question to demonstrate the effectiveness of angular indicators in increasing classification performances, using a supervised classifier based on Artificial Neural Networks and thus determining the most suitable signals.

Suggested Citation

  • Semchedine Fedala & Didier Rémond & Rabah Zegadi & Ahmed Felkaoui, 2018. "Contribution of angular measurements to intelligent gear faults diagnosis," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 1115-1131, June.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:5:d:10.1007_s10845-015-1162-1
    DOI: 10.1007/s10845-015-1162-1
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    Citations

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    Cited by:

    1. Yiping Gao & Liang Gao & Xinyu Li & Yuwei Zheng, 2020. "A zero-shot learning method for fault diagnosis under unknown working loads," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 899-909, April.
    2. Shaohua Huang & Yu Guo & Nengjun Yang & Shanshan Zha & Daoyuan Liu & Weiguang Fang, 2021. "A weighted fuzzy C-means clustering method with density peak for anomaly detection in IoT-enabled manufacturing process," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1845-1861, October.

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