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Low Complexity Non-Linear Spectral Features and Wear State Models for Remaining Useful Life Estimation of Bearings

Author

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  • Eoghan T. Chelmiah

    (Faculty of Engineering, South East Technological University, Carlow Campus, Kilkenny Rd, R93 V960 Carlow, Ireland)

  • Violeta I. McLoone

    (Faculty of Engineering, South East Technological University, Carlow Campus, Kilkenny Rd, R93 V960 Carlow, Ireland)

  • Darren F. Kavanagh

    (Faculty of Engineering, South East Technological University, Carlow Campus, Kilkenny Rd, R93 V960 Carlow, Ireland)

Abstract

Improving the reliability and performance of electric and rotating machines is crucial to many industrial applications. This will lead to improved robustness, efficiency, and eco-sustainability, as well as mitigate significant health and safety concerns regarding sudden catastrophic failure modes. Bearing degradation is the most significant cause of machine failure and has been reported to cause up to 75% of low-voltage machine failures. This paper introduces a low complexity machine learning (ML) approach to estimate the remaining useful life (RUL) of rolling element bearings using real vibration signals. This work explores different ML recipes using novel feature engineering coupled with various k -Nearest Neighbour ( k -NN), and Support Vector Machines (SVM) kernel and weighting functions in order to optimise this RUL approach. Original non-linear wear state models and feature sets are investigated, the latter are derived from Short-time Fourier Transform (STFT) and Hilbert Marginal Spectrum (HMS). These feature sets incorporate one-third octave band filtering for low complexity multivariate feature subspace compression. Our proposed ML algorithm stage has employed two robust supervised ML approaches: weighted k -NN and SVM. Real vibration data were drawn from the Pronostia platform to test and validate this prognostic monitoring approach. The results clearly demonstrate the effectiveness of this approach, with classification accuracy results of up to 82.8% achieved. This work contributes to the field by introducing a robust and computationally inexpensive method for accurate monitoring of machine health using low-cost vibration-based sensing.

Suggested Citation

  • Eoghan T. Chelmiah & Violeta I. McLoone & Darren F. Kavanagh, 2023. "Low Complexity Non-Linear Spectral Features and Wear State Models for Remaining Useful Life Estimation of Bearings," Energies, MDPI, vol. 16(14), pages 1-20, July.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5312-:d:1191679
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    References listed on IDEAS

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    1. Wang, Yung-Hung & Yeh, Chien-Hung & Young, Hsu-Wen Vincent & Hu, Kun & Lo, Men-Tzung, 2014. "On the computational complexity of the empirical mode decomposition algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 400(C), pages 159-167.
    2. Li-Ching Wu & Hsin-Hao Chen & Jorng-Tzong Horng & Chen Lin & Norden E Huang & Yu-Che Cheng & Kuang-Fu Cheng, 2010. "A Novel Preprocessing Method Using Hilbert Huang Transform for MALDI-TOF and SELDI-TOF Mass Spectrometry Data," PLOS ONE, Public Library of Science, vol. 5(8), pages 1-15, August.
    3. Oscar Duque-Perez & Carlos Del Pozo-Gallego & Daniel Morinigo-Sotelo & Wagner Fontes Godoy, 2019. "Condition Monitoring of Bearing Faults Using the Stator Current and Shrinkage Methods," Energies, MDPI, vol. 12(17), pages 1-13, September.
    Full references (including those not matched with items on IDEAS)

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