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

Eoghan T. Chelmiah (), Violeta I. McLoone and Darren F. Kavanagh ()
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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

Energies, 2023, vol. 16, issue 14, 1-20

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.

Keywords: condition-based monitoring (CbM); feature extraction; Hilbert-Huang transform (HHT); Hilbert marginal spectrum (HMS); k-Nearest Neighbour (kNN); machine learning (ML); mechanical bearings; prognostics; rotating machine; support vector machine (SVM); remaining useful life (RUL) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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