Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression

TLDR

Detection, diagnosis, and prognosis of bearing degradation are critical for enhancing the reliability and safety of electrical machines, particularly in key industrial sectors. This study introduces a novel monitoring framework that integrates the Hilbert–Huang transform, support vector machine, and support vector regression for ball bearings. The framework extracts health indicators from vibration signals via HHT, classifies degradation states with SVM, and predicts remaining useful life using SVR, validated on experimental data from degraded bearings. Experimental results demonstrate that combining HHT, SVM, and SVR improves the detection, diagnosis, and prognosis of bearing degradation.

Abstract

The detection, diagnostic, and prognostic of bearing degradation play a key role in increasing the reliability and safety of electrical machines, especially in key industrial sectors. This paper presents a new approach that combines the Hilbert-Huang transform (HHT), the support vector machine (SVM), and the support vector regression (SVR) for the monitoring of ball bearings. The proposed approach uses the HHT to extract new heath indicators from stationary/nonstationary vibration signals able to tack the degradation of the critical components of bearings. The degradation states are detected by a supervised classification technique called SVM, and the fault diagnostic is given by analyzing the extracted health indicators. The estimation of the remaining useful life is obtained by a one-step time-series prediction based on SVR. A set of experimental data collected from degraded bearings is used to validate the proposed approach. The experimental results show that the use of the HHT, the SVM, and the SVR is a suitable strategy to improve the detection, diagnostic, and prognostic of bearing degradation.