Nonlinear dynamic mechanical response analysis of dual-segment single-span rotor-bearing system under normal condition and misalignment fault

Ma, Chenyuan; Zhao, Dingtao; Sun, Wei; Liao, Wei‐Li; Xiao, Yang; He, Xu

doi:10.1007/s00419-022-02305-z

Cited by 1 publication

(1 citation statement)

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“…Due to the harsh working environment or changes in load, as well as various other reasons, rotating machinery equipment may experience faults, such as rotor imbalance, spindle misalignment, and loose foundation 9 , 10 . These faults cause radial vibration, axial vibration, and torque vibration in the system, among which radial vibration is the most likely to occur.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear mechanical response analysis and convolutional neural network enabled diagnosis of single-span rotor bearing system

Qian,

Cai,

Ran

et al. 2024

Sci Rep

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The wide application of rotating machinery has boosted the development of electricity and aviation, however, long-term operation can lead to a variety of faults. The use of different measures to deal with corresponding malfunctions is the key to generating benefits, so it is significant to carry out the fault diagnosis of rotating machinery. In this work, a test bench for single-span rotor bearings was established, three faults, including spindle bending, spindle crack without end loading and spindle crack with end loading, are experimental analyzed with basic mechanical response. Moreover, a diagnosis is performed using a convolutional neural network, according to the differences in mechanical responses of the three faults obtained from experiments. For three faults, the change in the properties of spindle itself results in different axis trajectories and spectra. Compared with spindle bending fault, spindle crack fault not only cause 1×, 2×, 3× frequency component excitation, also 4×, 5× frequency component excitation. Additionally, the classification accuracy of the training set and the test set under machine learning for the three types of working conditions is 100%. This indicates that the network can significantly identify signal features so as to make effective fault classification.

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Section: Introductionmentioning

confidence: 99%