Blade Tip Timing Signal Filtering Method Based on Sampling Aliasing Frequency Map

Cao, Jiahui; Tian, Shu; Yang, Zhibo; Teng, Guangrong; Li, Haoqi; Jin, Ruochen; Yan, Ruqiang; Chen, Xuefeng

doi:10.1109/tim.2022.3156979

Cited by 9 publications

(1 citation statement)

References 24 publications

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“…Therefore, new noise processing methods for BTT signals need to be developed. Cao 21 proposed two filtering methods for the BTT signal based on sampling aliasing frequency (SAFE) map, which can extract synchronous vibration and denoise the asynchronous vibration due to the clear distribution of different components in the SAFE map. Wang 18 proposed a robust sparse representation model based on Mixture of Gaussian (MoG) to reduce the impact of complex noise caused by speed fluctuation and casing vibration on the BTT sparse model.…”

Section: Introductionmentioning

confidence: 99%

A novel method for noise reduction of blade tip timing signals based on sparse representation and dictionary learning

Zheng,

Fang,

Duan

et al. 2023

Journal of Low Frequency Noise, Vibration and Active Control

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Blade Tip Timing Technology (BTT) is a new type of blade vibration measurement method with non-contact measurement capabilities, which is associated with high efficiency and convenience. However, due to under-sampling feature of the BTT signal, the method can only measure part of the vibration information. Therefore, various BTT spectral reconstruction algorithms have been developed based on the under-sampling feature and sparsity of the blade vibration spectrum. Because of the harsh working environment of the blades, BTT signal typically contains high-level Gaussian noise in actual measurement, which significantly reduces frequency-domain sparsity and even aliases the vibration modals, resulting in a decrease in the performance of sparse reconstruction algorithms. In this paper, a multi-sequences BTT signal noise reduction method is proposed, which is based on sparse representation to suppress Gaussian noise. Through appropriately modifying the constraint in the ℓ0 optimization problem, the method filters out the noise elements in the sparse vector of the BTT signal. Then, denoising is completed by sparse inverse transformation. Finally, a global average-based singular value decomposition dictionary learning algorithm (GA-K-SVD) is proposed to generate an over-complete sparse dictionary which is adaptable to the original signal, to increase the effectiveness of GA-K-SVD. At last, simulation and experience are carried out to verify the performance optimization effect for sparse reconstruction algorithm and the effectiveness of proposed noise reduction method.

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Section: Introductionmentioning

confidence: 99%