Remaining useful life prediction of rolling bearings based on refined composite multi-scale attention entropy and dispersion entropy

TL;DR

This paper introduces a novel multi-modal entropy-based feature extraction method for more accurate remaining useful life prediction of rolling bearings, leveraging advanced signal decomposition and fusion techniques.

Contribution

It proposes a new fusion of multi-scale attention entropy and dispersion entropy for better degradation feature extraction from vibration signals.

Findings

Improved RUL prediction accuracy across different operating conditions.

Effective extraction of degradation features using the proposed FMME method.

Enhanced health indicator robustness through entropy-based fusion.

Abstract

Remaining useful life (RUL) prediction based on vibration signals is crucial for ensuring the safe operation and effective health management of rotating machinery. Existing studies often extract health indicators (HI) from time domain and frequency domain features to analyze complex vibration signals, but these features may not accurately capture the degradation process. In this study, we propose a degradation feature extraction method called Fusion of Multi-Modal Multi-Scale Entropy (FMME), which utilizes multi-modal Refined Composite Multi-scale Attention Entropy (RCMATE) and Fluctuation Dispersion Entropy (RCMFDE), to solve the problem that the existing degradation features cannot accurately reflect the degradation process. Firstly, the Empirical Mode Decomposition (EMD) is employed to decompose the dual-channel vibration signals of bearings into multiple modals. The main modals are then selected for further analysis. The subsequent step involves the extraction of RCMATE and RCMFDE from each modal, followed by wavelet denoising. Next, a novel metric is proposed to evaluate the quality of degradation features. The attention entropy and dispersion entropy of the optimal scales under different modals are fused using Laplacian Eigenmap (LE) to obtain the health indicators. Finally, RUL prediction is performed through the similarity of health indicators between fault samples and bearings to be predicted. Experimental results demonstrate that the proposed method yields favorable outcomes across diverse operating conditions.