Anti-Noise Performance and Parameter Estimation Accuracy of FFT and FT Discrete Spectrum Correction

Ding, Kang; Zhu, Wei; Yang, Zhongmin; Li, Weihua

doi:10.1109/cisp.2009.5303531

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…In the process of the steady sine test, the sampling frequencies were always set as integer times of the operational ones. As in this way, the error induced by spectrum leakage could be reduced [4] . The linear relation between exciting force and acceleration response is validated through experiment.…”

Section: Experimental Investigation Of Ribbed Cylindermentioning

confidence: 93%

Experimental Investigation of Ribbed Cylinder's Vibroacoustics Transmissibility

Xie

Zhou

2012

AMR

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Exciting force's accurate measurement is crucial to the structure-born sound radiation. And the force sensor is calibrated by the dynamic force calibration equipment. Exciting force is accurately measured on the ribbed cylinder’s forced vibration and sound radiation. Based on the steady sine and random white noise exciting, the frequency response functions of the acceleration and sound pressure are obtained. Results obtained from the two methods are shown to be in good agreement with each other. According to the acceleration’s frequency response functions and sound pressure’s frequency response functions, it is obvious that the peak frequencies in the two frequency response functions are corresponding to each other. And the correspondence relationship could be used to help control the radiated noise.

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Section: Experimental Investigation Of Ribbed Cylindermentioning

confidence: 93%

Experimental Investigation of Ribbed Cylinder's Vibroacoustics Transmissibility

Xie

Zhou

2012

AMR

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“…Traditional mathematical processing methods mainly include linear stationary mathematical transformation methods and non-stationary, non-Gaussian distribution and nonlinear random signal processing methods. The linear stationary mathematical transformation methods are represented by time domain statistical analysis, 1 frequency domain statistical analysis, 2 Fourier transform analysis, 3 time series model analysis method, 4 refined spectrum analysis, 5 holographic spectrum analysis, 6 singular spectrum noise reduction method, 7 matching tracking analysis, 8 and geometric fractal analysis method. 9 Non-stationary, non-Gaussian distribution, and nonlinear random signal processing methods are represented by high-order spectral analysis, 10 principal component analysis, 11 short-time Fourier transform (STFT), 12 Wigner–Ville distributing, 13 wavelet transform (WT), 14 cyclic stationary analysis method, 15 random resonance method, 16 empirical mode decomposition (EMD), 17 Hilbert–Huang transform (HHT), 18 and the second-generation WT method.…”

Section: Introductionmentioning

confidence: 99%

Comparisons of different deep learning-based methods on fault diagnosis for geared system

Han

Yang

Ren

et al. 2019

International Journal of Distributed Sensor Networks

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The running state of a geared transmission system affects the stability and reliability of the whole mechanical system. It will greatly reduce the maintenance cost of a mechanical system to identify the faulty state of the geared transmission system. Based on the measured gear fault vibration signals and the deep learning theory, four fault diagnosis neural network models including fast Fourier transform–deep belief network model, wavelet transform–convolutional neural network model, Hilbert-Huang transform–convolutional neural network model, and comprehensive deep neural network model are developed and trained respectively. The results show that the gear fault diagnosis method based on deep learning theory can effectively identify various gear faults under real test conditions. The comprehensive deep neural network model is the most effective one in gear fault recognition.

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