Turbo-generator rotor fault diagnosis via vibration mode decomposition signatures
Year: 2026
Type: article
Abstract: Frequent load fluctuations from renewable-energy integration increase operational stress on steam turbo-generators and raise the risk of rotor inter-turn short circuits. This paper proposes a measurement-oriented multimodal framework for robust fault-state estimation under complex operating conditions. A fault-sensitive measurand derived from 50 Hz and 100 Hz vibration components at the stator excitation end, is defined and theoretically linked to rotor winding faults. An electromagnetic-mechanical coupled model in ANSYS reveals how rotor inter-turn faults modulate stator vibration spectra; modal characteristics are validated on a 600 MW turbo-generator. A formal measurement model maps the chain from vibration sensing and speed-reference acquisition to feature transformation and fault-state estimation. Variational mode decomposition parameters are optimized via the arithmetic optimization algorithm; band-pass amplitude sequences yield multimodal time-frequency features, processed by a multimodal feature-enhanced temporal neural network. Traceability from transducer to processed feature is ensured, and uncertainty is quantified across signal-to-noise ratio variation, frequency resolution, modal separation, repeatability/reproducibility, and confidence intervals of estimates. Under test conditions, the method achieves robust fault-state estimation and superior noise immunity versus baseline models, demonstrating strong potential for practical rotor winding fault diagnosis in large turbo-generators.
Source: Measurement
Authors: Renzeng Yang, Shuang Peng, Xingda Xie, Yipeng Zhang
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,760
Awards: G1308744533, G3086162730, G7853259779
OpenAlex ID: W7140196503