A Wavelet Packet Transform Based Deep Feature Transfer Learning Method for Bearing Fault Diagnosis Under Different Working Conditions

Yu, Xiao; Liang, Zhongting; Wang, Youjie; Yin, Hongshen; Liu, Xiaowen; Yu, Wanli; Huang, Yanqiu

doi:10.2139/ssrn.4040036

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2024

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“…However, the collected vibration signals usually contain many useless noise signals, which seriously affects the extraction of fault characteristics of the vibration signals. In recent years, many scholars have carried out diagnostic analyses of rolling bearing faults using wavelet transform, but due to limitations such as single diagnostic conditions, it lacks operability in reality [5][6][7][8][9] .…”

Section: Introductionmentioning

confidence: 99%

Application of K-Core analysis based on complex network community structure to rolling bearing fault diagnosis

Sun,

Cai,

Gao

et al. 2024

J. Phys.: Conf. Ser.

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A complex network is a method to analyze nonlinear time series on multiple temporal and spatial scales. K-core analysis of several typical nonlinear series was studied, and then based on this, a fast algorithm method was employed to divide the community structure. According to the definition of k-core, the rolling bearings complex network was divided into different cores at the router level, and the main characteristic quantities such as degree distribution and clustering coefficient of every k-core can be analyzed. The results show that there is a corresponding relationship between the characteristic parameters and the community structure of the bearing. The analysis results show that compared with the traditional method, the method of complex network community structure and k-core analysis has a better diagnosis effect.

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