Wind turbine bearings operate long-term under complex and variable operating conditions, where fault impulse characteristics are easily submerged by strong noise. Traditional graph signal processing-based bearing fault diagnosis methods are limited by fixed graph topology, empirical feature selection and poor noise rob...
Rolling bearing faults typically exhibit sideband structures in the frequency domain, yet conventional blind deconvolution methods operate in the time domain and rely on prior fault periods. To reduce this dependence and broaden applicability, this paper presents a frequency-domain blind deconvolution method based on w...
You-Sheng Yang, Lei Feng, Yi-Ding Liu et al.· Signals· 0 citations
Precise diagnosis of rolling bearing health conditions is essential for reducing the maintenance costs of rotating machinery. To overcome the limitations of conventional spectrum segmentation methods, which rely on fixed filter banks and struggle to adapt to complex modulation characteristics, this paper proposes a cep...
Yaxin Liu, Pengcheng Zhao, Chen-Yu Wang et al.· Measurement science and tech...· 0 citations
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