Adaptive Iterative Approach for Efficient Signal Processing of Blade Tip Timing

Li, Haoqi; Yang, Zhibo; Wu, Shuming; Wang, Zengkun; Tian, Shu; Yan, Ruqiang; Chen, Xuefeng

doi:10.1109/tim.2021.3107588

Cited by 11 publications

(7 citation statements)

References 39 publications

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“…In the formula, the matrix E is the exponential matrix of K × N, and each element is E k,n = e −j2π fnt k . The matrix R is the autocorrelation matrix of the vector x(t k ), which is a Hermit matrix of K × N. The calculations of each element in R are shown in formula (15):…”

Section: Extended Fourier Transformmentioning

confidence: 99%

“…Rigosi et al [14] presented a method to extract the main parameters (amplitude and frequency) of the resonance condition from the tip timing measurements. Li et al [15] proposed an adaptive iterative approach based on the iterative reweighted least squares periodogram (IRLSP) that combines a priori information of the blade. It can improve the computational efficiency of processing the BTT signal and the effect of anti-aliasing.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Extended Fourier Transformmentioning

confidence: 99%

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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show abstract

“…These peaks were local and were used to derive the vibration characteristics of a particular blade. The blade probe data were then processed by the iterative reweighted least-squares periodogram (IRLSP) algorithm [ 18 ]. An illustration of the blade tip displacements of eight blades fusing all data from five probes and the shaft rotation speed is given in Figure 18 .…”

Section: Experimental Investigationmentioning

confidence: 99%

“…For this particular vibration signal, the emphasis of signal processing is on extraction of the natural frequency of the blade using different methods [ 16 , 17 ]. In [ 18 ], the steps of extracting the blade vibration frequency from the BTT signal were detailed as follows: data acquisition, trend filtering, resonance identification and spectrum reconstruction. The commonly used trend filtering algorithms include Savitzky-Golay filtering, singular spectrum analysis (SSA) [ 19 ], exponential moving average and so on.…”

Section: Introductionmentioning

confidence: 99%

“…Wang [ 23 ] established a robust sparse representation model for the BTT data. Wu and Li successively proposed the adaptive reweighted least-squares periodogram and adaptive iterative approach (AIA) methods on the basis of the least-squares periodogram (LSP), which can efficiently and accurately extract the vibration frequency and amplitude of the blades [ 18 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Blade Vibration Monitoring Experimental System Based on Blade Tip Sensing

Tian

Yang

2022

Materials

Self Cite

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Due to its non-intrusive manner, blade tip timing (BTT) is considered a potential tool for the condition monitoring of turbomachinery. The challenge of BTT relates to significant under-sampled signal processing, which is induced by a lower number of probes. Signal processing assumes that the ability of the hardware system can meet the requirements of the software algorithm. The abilities of the hardware, including the time resolution of the data acquisition system (DAS) and the dynamic characteristics of rigs, are compromised, particularly when the rotating speed increases. This increase in speed causes two problems for BTT: (1) the rig is less stable, due to the reduction of dynamic stiffness; (2) the time resolution of the DAS can be inadequate for identification. To promote the performance of the hardware system, here a BTT rig was designed with high dynamic performance, including a new DAS with a time resolution of 10 ns. A variety of commonly used BTT signal processing methods are used to analyze the experimental data and verify the good reliability and validity of the experimental platform.

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A baseline drift removal algorithm based on cumulative sum and downsampling for hydroacoustic signal

Zhang

Zhu

et al. 2023

Measurement

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Adaptive Iterative Approach for Efficient Signal Processing of Blade Tip Timing

Cited by 11 publications

References 39 publications

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

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

A Novel Blade Vibration Monitoring Experimental System Based on Blade Tip Sensing

A baseline drift removal algorithm based on cumulative sum and downsampling for hydroacoustic signal

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