Reconstruction of blade tip-timing signals based on the MUSIC algorithm

Liu, Zhibo; Duan, Fajie; Niu, Guangyue; Ye, Dechao; Feng, Junnan; Cheng, Zhonghai; Fu, Xiao; Jiang, Jiajia; Zhu, Jing; Li, Meiru

doi:10.1016/j.ymssp.2021.108137

Cited by 18 publications

(3 citation statements)

References 18 publications

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“…However, BTT is a severely undersampled measurement method, which provides great challenges for the parameters identification of vibration based on BTT. In order to extract blade vibration parameters from undersampled signals, parameters identification methods such as Circumferential Fourier Fitting (CFF) method, single-parameter method, double-parameter method, and spatial methods have been developed [8][9][10][11][12][13]. The CFF method is simple and requires less data, so it is widely used in engineering.…”

Section: Introductionmentioning

confidence: 99%

Parameters Identification of Blade Multi-mode Coupled Vibration Based on Blade Tip Timing

Zhi

Duan

Li³

et al. 2023

J. Phys.: Conf. Ser.

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Health monitoring is crucial for the safe operation of rotating machinery. As an effective technique for measuring the vibration of rotating blades, blade tip timing (BTT) is widely used since its advantages of non-contact and low intervention. During the operation of high-speed blades, the multi-mode coupled synchronous vibration will occur. However, the existing BTT-based methods cannot identify the multi-mode coupled vibration parameters. To solve the problem, a multi-mode coupled vibration model of the blade based on a slender cantilever beam is established, and the contribution of each mode to the blade vibration response is considered. A new Circumferential Fourier Fit (CFF) method for parameters identification of blade multi-mode coupled synchronous vibration is proposed. According to the proposed method, the reconstruction of multi-mode coupled synchronous vibration signals can be realized. Finally, the accuracy of the proposed method is verified by simulation data.

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Section: Introductionmentioning

confidence: 99%

Parameters Identification of Blade Multi-mode Coupled Vibration Based on Blade Tip Timing

Zhi

Duan

Li³

et al. 2023

J. Phys.: Conf. Ser.

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“…Chen et al [1] presented a novel interpolation method for wideband signal reconstruction. Liu et al [19] proposed a new reconstruction method based on the MUSIC algorithm. This allows the traditional MUSIC method to overcome spectrum aliasing without expanding the snapshot matrix.…”

Section: Introductionmentioning

confidence: 99%

Parameter identification method of nonuniform and under-sampled blade tip timing based on extended DFT and compressed sensing

Zhang,

Ding

et al. 2023

Meas. Sci. Technol.

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Vibration amplitude and frequency are the two most important indicators that characterize the health status of high-speed rotating blades, but the signal obtained by blade tip timing (BTT) technology, one of the best rotating blade vibration monitoring methods, is seriously nonuniform and under-sampled, which makes these two indicators difficult to identify. In view of this problem, the paper proposes a parameter identification method for the nonuniform and under-sampled BTT signal based on extended Discrete Fourier transform (EDFT) and Compressed Sensing (CS), with the Fourier integral transformation as the goal. It realizes the frequency analysis of nonuniform under-sampled signals by constructing and optimizing the transformation basis function instead of the exponential basis in the traditional FFT transformation in the extended frequency range, and then constructs a CS model through the obtained blade vibration frequency. The complete waveform of the blade vibration is restored by using a small number of under-sampled signals, thus obtaining the blade vibration amplitude and vibration frequency. On the one hand, the method proposed in this paper breaks through the limitation of Nyquist's sampling theorem, and the number of analytical spectral lines is no longer limited to the number of sampling points, which improves the frequency resolution. On the other hand, only a small number of measurement signals can be reconstructed to achieve a complete vibration signal. The feasibility and reliability of the proposed method are verified by mathematical modeling, simulation analysis, and experimental testing. The results indicate that when the number of sensors is greater than or equal to four, the time domain and frequency domain signals of blade vibration can be accurately analyzed based on the proposed method, the vibration amplitude error is less than 0.01 mm, the frequency error is less than 0.1 Hz, and it has good anti-interference performance.

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“…5 The under-sampling characteristics of BTT signals can be characterized by using compressed sensing theory during signal processing. [6][7][8][9][10] Lin 11 first proposed a multi-coset BTT sampling model applying compressed sensing to transform the infinite dimensional sparse representation model into a finite dimensional multiple measurement vector (MMV) model. The Orthogonal Matching Pursuit (OMP) method 12 was then used to reconstruct the blade multi-modal vibration spectrum.…”

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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Reconstruction of blade tip-timing signals based on the MUSIC algorithm

Cited by 18 publications

References 18 publications

Parameters Identification of Blade Multi-mode Coupled Vibration Based on Blade Tip Timing

Parameters Identification of Blade Multi-mode Coupled Vibration Based on Blade Tip Timing

Parameter identification method of nonuniform and under-sampled blade tip timing based on extended DFT and compressed sensing

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

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