Hilbert Marginal Spectrum for Failure Mode Diagnosis of Rotating Machines

Chelmiah, Eoghan T.; Kavanagh, Darren F.

doi:10.1109/iecon48115.2021.9589472

Cited by 4 publications

(1 citation statement)

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“…Similarly, a rich literature exists on the topic of feature extraction approaches on vibration signals for prognostic monitoring applications. These include statistical time-domain features [25][26][27] and timefrequency feature extraction methods such as Short-time Fourier Transform (STFT) [28][29][30], signal Envelope Analysis (EA) [31][32][33], Wavelet Transform (WT) [34,35], Feature Mode Decomposition (FMD) [36], and Empirical Mode Decomposition (EMD) methods [37][38][39][40]. In recent years, a variety of Machine Learning (ML) algorithms have been proposed to perform the task of bearing RUL predictions.…”

Section: Introductionmentioning

confidence: 99%

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

2023

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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.

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