Remaining useful life prediction of rolling bearings based on refined composite multi-scale attention entropy and dispersion entropy
<|reference_start|>Remaining useful life prediction of rolling bearings based on refined composite multi-scale attention entropy and dispersion entropy: 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.<|reference_end|>
@article{long2024remaining,
    title={Remaining useful life prediction of rolling bearings based on refined
  composite multi-scale attention entropy and dispersion entropy},
    author={Yunchong Long, Qinkang Pang, Guangjie Zhu, Junxian Cheng and Xiangshun
  Li},
    journal={arXiv preprint arXiv:2406.16967},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.16967},
    primaryClass={eess.SP cs.SY eess.SY}
}