An energy track method for early-stage rub-impact fault investigation of rotor system

Zhang, Xiantao; Yang, Yue; Shi, Mingming; Zhang, Yamei; Wang, Ping

doi:10.1016/j.jsv.2021.116545

Cited by 15 publications

(1 citation statement)

References 42 publications

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“…The axis trajectory contains very important information for fault diagnosis of rotor systems. The full spectrum analysis 7,32 and energy identification methods 33,34 were adopted to extract the shape characteristics of an axis trajectory to diagnose rub-impact faults for rotor systems. Some approaches based on orbit shape, 11 nonlinear output frequency response functions 13 and average rotor centerline plots 35 were used to detect misalignment faults.…”

Section: Introductionmentioning

confidence: 99%

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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