The wireless measuring instrument for hydroturbine axial alignment based on extreme value fast optimization

To mitigate the challenges of inaccuracies and inefficiencies in manual alignment of hydroturbine shafts, a wireless measuring instrument for axial alignment based on extremum fast optimization is designed. The proposed method introduces three primary innovations. First, a wireless sensing and measurement system is established by strategically deploying grating displacement sensors and angular encoders at multiple critical locations, integrated with LoRa wireless transmission technology. This configuration enables 360° continuous data acquisition, effectively eliminating the constraints of traditional eight-point discrete measurement methods that require fixed-point positioning and manual coordination. Second, an enhanced simulated annealing (SA) extremum optimization algorithm is developed, incorporating an adaptive step-size perturbation model, a multi-stage annealing process with a restart mechanism, and a flexible temperature decay strategy. These improvements address the common shortcomings of conventional SA algorithms, such as fixed step sizes that cause local optimum trapping and slow convergence, thereby enabling precise and rapid identification of extremum points in axis deflection curves. Third, sinusoidal function fitting is employed for deflection curve analysis, which, when combined with the optimized algorithm, allows direct determination of the maximum deviation direction, replacing the traditional multi-iteration adjustment process. The axial alignment experiments are conducted on the hydroturbine axis prototype, and the results show that the swing values obtained from the designed measuring instrument are consistent with the traditional eight-point method, achieving 99.8% consistency and improving alignment efficiency by 400%. The extremum identification accuracy reaches 0.001 mm, demonstrating that the proposed method establishes a novel and highly effective paradigm for intelligent hydroturbine shaft alignment.

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Publication Details

Journal
Review of Scientific Instruments
Published
2025-09-01
DOI
https://doi.org/10.1063/5.0272694
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
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article

The wireless measuring instrument for hydroturbine axial alignment based on extreme value fast optimization

X. R. Li, Ang Li, Peng Liang, Xi Wang et al.
Review of Scientific Instruments
Model Reduction and Neural Networks
article

The wireless measuring instrument for hydroturbine axial alignment based on extreme value fast optimization

X. R. Li, Ang Li, Peng Liang, Xi Wang, J. Zhu, Yongfei Wang
article en

Abstract

To mitigate the challenges of inaccuracies and inefficiencies in manual alignment of hydroturbine shafts, a wireless measuring instrument for axial alignment based on extremum fast optimization is designed. The proposed method introduces three primary innovations. First, a wireless sensing and measurement system is established by strategically deploying grating displacement sensors and angular encoders at multiple critical locations, integrated with LoRa wireless transmission technology. This configuration enables 360° continuous data acquisition, effectively eliminating the constraints of traditional eight-point discrete measurement methods that require fixed-point positioning and manual coordination. Second, an enhanced simulated annealing (SA) extremum optimization algorithm is developed, incorporating an adaptive step-size perturbation model, a multi-stage annealing process with a restart mechanism, and a flexible temperature decay strategy. These improvements address the common shortcomings of conventional SA algorithms, such as fixed step sizes that cause local optimum trapping and slow convergence, thereby enabling precise and rapid identification of extremum points in axis deflection curves. Third, sinusoidal function fitting is employed for deflection curve analysis, which, when combined with the optimized algorithm, allows direct determination of the maximum deviation direction, replacing the traditional multi-iteration adjustment process. The axial alignment experiments are conducted on the hydroturbine axis prototype, and the results show that the swing values obtained from the designed measuring instrument are consistent with the traditional eight-point method, achieving 99.8% consistency and improving alignment efficiency by 400%. The extremum identification accuracy reaches 0.001 mm, demonstrating that the proposed method establishes a novel and highly effective paradigm for intelligent hydroturbine shaft alignment.

Review of Scientific InstrumentsVol. 96(9)
Energy Research Institute (CN), Southwest Jiaotong University (CN)
Openalex Percentile: Top 81%
Model Reduction and Neural Networks
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