Modal parameter estimation of a reduced-scale rocket nozzle using blind source separation

Eitner, Marc A.; Sirohi, Jayant; Tinney, Charles E.

doi:10.1088/1361-6501/ab228f

Cited by 7 publications

(3 citation statements)

References 18 publications

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“…Recently, various methods for identifying modal parameters of time-invariant structures were proposed [7][8][9][10]. Additionally, the simplest and most typical one is the peak picking (PP) method [11].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Operational Modal Analysis under Non-White Noise Excitation Using Hybrid Neural Networks

et al. 2022

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To adaptively identify the modal parameters for time-invariant structures excited by non-white noise, this paper proposes a new operational modal analysis (OMA) method using hybrid neural networks. In this work, taking the acceleration response directly as the input data of the networks not only simplifies the data processing, but also retains all the characteristics of the data. The data processed by the output function is the output data of the network, and its peak corresponds to the modal frequency. The proposed output function greatly reduces the computational cost. In addition, a small sample dataset ensures that the hybrid neural networks identify the modal parameters with the highest accuracy in the shortest possible time. Interestingly, the hybrid neural networks combine the advantages of the convolutional neural network (CNN) and gate recurrent unit (GRU). To illustrate the advantages of the proposed method, the cantilever beam and the rudder surface excited by white and non-white noise are taken as examples for experimental verification. The results reveal that the proposed method has a strong anti-noise ability and high recognition accuracy, and is not limited by ambient excitation type.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Operational Modal Analysis under Non-White Noise Excitation Using Hybrid Neural Networks

et al. 2022

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“…Grotas et al [43] developed the constrained maximum likelihood (ML) estimator of the Laplacian matrix for this graph BSS problem with Gaussian-distributed states. Eitner et al [44] used two blind source separation algorithms to estimate the modal parameters of a reduced-scale rocket nozzle using only measurements of deformation. Ge and Jiang [9] proposed a framework based on joint blind source separation (JBSS) in order to solve the jamming suppression problem in the noise environment for the distributed radar with single transmitter and multiple receivers, where the multiple jamming enter into all the receivers through the main beam of the antennas.…”

Section: Introductionmentioning

confidence: 99%

Multisource Fault Signal Separation of Rotating Machinery Based on Wavelet Packet and Fast Independent Component Analysis

Miao

Zhao

Jia

et al. 2021

International Journal of Rotating Machinery

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The vibration signal of rotating machinery compound faults acquired in actual fields has the characteristics of complex noise sources, the strong background noise, and the nonlinearity, causing the traditional blind source separation algorithm not be suitable for the blind separation of rotating machinery coupling fault. According to these problems, an extraction method of multisource fault signals based on wavelet packet analysis (WPA) and fast independent component analysis (FastICA) was proposed. Firstly, according to the characteristic of the vibration signal of rotating machinery, an effective denoising method of wavelet packet based on average threshold is presented and described to reduce the vibration signal noise. In the method, the thresholds of every node of the best wavelet packet basis are acquired and averaged, and then the average value is used as a global threshold to quantize the decomposition coefficient of every node. Secondly, the mixed signals were separated by using the improved FastICA algorithm. Finally, the results of simulations and real rotating machinery vibration signals analysis show that the method can extract the rotating machinery fault characteristics, verifying the effectiveness of the proposed algorithm.

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“…Modal parameters (MPs) such as natural frequencies and damping ratios have a great significance in distinct fields of civil engineering because they allow characterizing and identifying different properties of civil structures, which can be used for vibration control [1][2][3][4], structural health monitoring [5][6][7], energy harvesters [8] and updating of analytical models [9][10][11]. Therefore, the accurate identification of MPs through ambient vibrations (operational modal analysis, OMA) and artificial vibrations (experimental modal analysis, EMA) is of paramount interest [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear mode decomposition-based methodology for modal parameters identification of civil structures using ambient vibrations

Yanez-Borjas

Amezquita-Sanchez

Valtierra-Rodríguez

et al. 2019

Meas. Sci. Technol.

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The accurate and effective identification of modal parameters such as natural frequencies and damping ratios represents a key issue for different applications in civil engineering, e.g. updating of analytical models, vibration control, and structural health monitoring. Therefore, the development and application of new methods to perform this task with high accuracy have attracted the interest of many researchers around the world. This paper presents a new methodology based on the nonlinear mode decomposition to estimate the natural frequencies and damping ratios of civil structures subjected to ambient vibrations. This type of signals represents a challenge due to that the measured structural response is embedded into noisy signals with low amplitude. In order to validate the methodology, three different examples are used: two based on analytical models and one based on real data. Results show that the proposal can estimate the natural frequencies, especially the closely spaced modes, and damping ratios of civil structures subjected to ambient vibrations with a high accuracy.

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Modal parameter estimation of a reduced-scale rocket nozzle using blind source separation

Cited by 7 publications

References 18 publications

An Adaptive Operational Modal Analysis under Non-White Noise Excitation Using Hybrid Neural Networks

An Adaptive Operational Modal Analysis under Non-White Noise Excitation Using Hybrid Neural Networks

Multisource Fault Signal Separation of Rotating Machinery Based on Wavelet Packet and Fast Independent Component Analysis

Nonlinear mode decomposition-based methodology for modal parameters identification of civil structures using ambient vibrations

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