Dynamic Characteristics Analysis of a Coupled Multi-crack Rotor System

Xiong, Libo; Hua, Chunrong; Yang, Funing; Dong, Dawei; Ouyang, Huajiang

doi:10.1088/1742-6596/2184/1/012040

Cited by 2 publications

(3 citation statements)

References 23 publications

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“…This is because the interaction between multiple cracks increases flexibility and alters the nonlinearity of stiffness of the rotor system. 55 In the cases of coupled faults, Figure 6(b) and (f), (d) and (i), (e) and (j), (g) and (k), (h) and (l) show that the misalignment fault only has a slight effect on the trajec-tories of the cracked rotor. Therefore, the shape change of axis trajectories cannot reflect the diverse fault conditions of the rotor system.…”

Section: Dynamic Responses Analysismentioning

confidence: 92%

“…The rotor system model 15,55 consists of a shaft (discretized into 60 Timoshenko beam elements), two disks (regarded as rigid bodies), and two ball bearings (simplified as springs and dampers), as shown in Figure 1. Some of the systemic physical parameters are given in Table 1.…”

Section: Multi-fault Classification Based On Trajectory Phase Featurementioning

confidence: 99%

“…The occurrence of one or multiple faults will change the nonlinear stiffness and Rayleigh damping coefficients of a rotor system, 55 which will aggravate the complexity of nonlinear vibration responses, resulting in more harmonic components in the frequency spectrum and more complex of the axis trajectory shape.…”

Section: Dynamic Responses Analysismentioning

confidence: 99%

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Multi-fault classification of rotor systems based on phase feature of axis trajectory in noisy environments

Hua

Xiong

et al. 2023

Structural Health Monitoring

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As it is difficult to distinguish multiple rotor faults with similar dynamic phenomena in noisy environments, a multi-fault classification method is proposed by combining the extracted trajectory phase feature, a parameter-optimized variational mode decomposition (VMD) method and a light gradient boosting machine (LightGBM) model. The trajectory phase feature is extracted from an axis trajectory by fusing the frequency, amplitude, and phase information related to rotor motion and can comprehensively describe the dynamic characteristics induced by different rotor faults. First, the vibration displacement signals in two orthogonal directions are collected to construct the axis trajectories with 12 rotor states including healthy, unbalance, misalignment, single crack, multiple cracks, and a mixture of them. Second, the trajectory phase feature is extracted from the vectorized axis trajectories, and the frequency spectra of trajectory phase angles under different rotor faults are analyzed through Fourier transform. Finally, a parameter-optimized VMD method combined with a LightGBM model is applied to classify multiple faults of rotor systems in different noisy environments based on the extracted trajectory phase feature. The 12 rotor states can be classified into nine categories based on the harmonic information of 1X–7X components (X is the rotating frequency of a rotor system) and other components with smaller amplitudes in the frequency spectra of trajectory phase angles. The average classification accuracy of the 12 rotor states exceeds 93.0%, and the recognition rate for each kind of fault is greater than 77.5% in noisy environments. The simulated and experimental results demonstrate the effectiveness and adaptability of the proposed multi-fault classification method. This work can provide a reference for the condition monitoring and fault diagnosis of rotor systems in engineering.

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Section: Dynamic Responses Analysismentioning

confidence: 92%

Section: Multi-fault Classification Based On Trajectory Phase Featurementioning

confidence: 99%