Influences of blade crack on the coupling characteristics in a bladed disk with elastic support

Wu, Zhiyuan; Yan, Han; Zhao, Lingmin; Yan, Ge; Chen, Ao; Hu, Haifeng; Zhang, Wen Ming

doi:10.1016/j.ast.2023.108135

Cited by 19 publications

(1 citation statement)

References 51 publications

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“…For rotating machinery, such as an aero-engine, gas turbine, or machine tool [1], advanced and effective on-line condition monitoring methods are essential to detect the faults and deterioration of rotor systems, including shaft fractures, turbine disc cracks, rubimpact between the rotor and stator, and bearing spalling [2]. On-line condition monitoring is crucial to achieve the safe operation of rotor systems, reduce downtime and improve production efficiency [3].…”

Section: Introductionmentioning

confidence: 99%

On-line condition monitoring for rotor systems based on nonlinear data-driven modelling and model frequency analysis

Zhao,

Liu,

Zhang

et al. 2024

Nonlinear Dyn

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This paper proposes a novel on-line rotor system condition monitoring approach using nonlinear data-driven modelling and model frequency analysis. First, the dynamic process model of the vibration transmission path between the vibration measurement points of two fulcrum structures is established by utilizing nonlinear data-driven modelling. Then, the unique frequency properties are extracted from the established model to reveal, in real time, the health condition of the rotor system. Finally, using the frequency properties as features, the unsupervised learning technology is applied to the on-line monitoring of the rotor system. Compared to conventional condition monitoring methods, the proposed approach can output an early warning 26 min before a shaft fracture occurs, without generating false alarms. Consequently, this approach can greatly enhance diagnostic accuracy, demonstrating its potential to contribute to the advancement of rotor system condition monitoring techniques.

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Section: Introductionmentioning

confidence: 99%