Blade vibration study by spectral analysis of tip-timing signals with OMP algorithm

Bouchain, Antoine; Picheral, José; Lahalle, Elisabeth; Chardon, Gilles; Vercoutter, Agathe; Talon, Arnaud

doi:10.1016/j.ymssp.2019.04.063

Cited by 62 publications

(14 citation statements)

References 22 publications

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“…Wu et al [16] designed an adaptively reweighted least squares periodogram algorithm to identify parameters of the resonant vibration. Bouchain et al [17] proposed a spectral analysis approach of tip timing signals with an orthogonal matching pursuit (OMP) algorithm. The method does not need to set up regularization parameters and can reduce frequency aliasing by introducing 0-norm as sparsity prior.…”

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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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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“…At present, the research on the BTT method is mainly focused on the level of signal processing [ 13 , 14 ]. Due to the limitation of the installation location and the number of optical probes, the blade tip vibration signals measured by the BTT method have the characteristics of non-uniform sampling and sub-Nyquist sampling [ 15 ]. 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 ].…”

Section: Introductionmentioning

confidence: 99%

“…They used the Circumferential Fourier Fit (CFF) method to identify the resonant frequency, amplitude, and damping information through the BTT data [ 21 ]. Thereafter, Pan proposed a compressed sensing method for reconstructing BTT signals on the basis of dictionary learning [ 15 ], and Bouchain presented a new approach on the basis of an l 0 -regularization solved with the orthogonal matching pursuit (OMP) algorithm adapted to the model [ 22 ]. Wang [ 23 ] established a robust sparse representation model for the BTT data.…”

Section: Introductionmentioning

confidence: 99%

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

Tian

Yang

2022

Materials

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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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“…The CS theory is combined with reconstruction algorithms for dealing with the minimum norm problem, resulting in realized reconstructed signals. Based on CS theory, the parallel FISTA algorithm is proposed for construction of vibration bearing, and Orthogonal matching pursuit (OMP) based on CS theory can be used to extract meaningful transient representation of bearing vibration signals [10,11]. Furthermore, the k-means singular value decomposition algorithm was proposed to solves the sparse atom problem regarding the gear fault diagnosis, but there exists and issue when tackling a large number of collected signals [12,13].…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of bearing vibration signals based on a reconstruction algorithm with multiple side Information and CEEMDAN method

Gao¹,

Ma²,

Chanwimalueang³

et al. 2020

J. vibroeng.

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When bearing vibration of instruments is monitored, a large number of data are produced. This requires a massive capacity of storage and high bandwidth of data transmission whereby costs and complex installation are concerned. In this study, we aim to propose an effective framework to address such the amount of bearing signals to which only meaningful information is extracted. Based on the compressed sensing (CS) theory. We proposed a reconstruction algorithm based on the multiple side information signal (RAMSI) with a purpose to effectively obtain important information from recorded bearing signals. In the process of sparse optimization, the RAMSI algorithm was implemented to solve the − 1 minimization problem with the weighting adaptive multiple side information signals. Wavelet basis and Hartley matrix were applied for the reconstruction process, for which the effective sparse optimization processing of bearing signals was able to adaptively computed. The performance of our RAMSI-based CS theory was compared with the basis pursuit (BP) which is based on the alternating direction method of multiplier (ADMM) and orthogonal matching pursuit (OMP). The error indices of the reconstruction algorithms were evaluated. This proves that the performance of the sparse optimization algorithm from our proposed framework is superior to the BP based on the ADMM and OMP algorithm. After recovering vibration signals, some strong noise caused by the incipient fault characteristic of the bearing. The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method was performed to extract the bearing fault component from such noise. In terms of performance, the CEEMDAN method was compared to the standard ensemble empirical mode decomposition (EEMD) method. The results show that the CEEMDAN method yields a better decomposition performance and is able to extract meaningful information of bearing fault characteristic.

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Blade vibration study by spectral analysis of tip-timing signals with OMP algorithm

Cited by 62 publications

References 22 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

Fault diagnosis of bearing vibration signals based on a reconstruction algorithm with multiple side Information and CEEMDAN method

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