Cyclostationary error analysis and filter properties in a 3D wavelet coding framework

Leonardi, Riccardo; Signoroni, Alberto

doi:10.1016/j.image.2006.06.001

Cited by 5 publications

(3 citation statements)

References 29 publications

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“…Subsequently, this sound pressure becomes a modulation function of the sound pressure generated by the relative motion between the bearing fault source and the acquisition device. The amplitude-corrected signal [14] can be expressed as shown in Equation (18). The acoustic signal obtained from Equation ( 18) undergoes theoretical corrections in both time and amplitude, resulting in an acoustic signal free from Doppler distortion.…”

Section: Magnitude Correctionmentioning

confidence: 99%

“…They proposed a set of filtering methods to decompose the second-order cyclic smooth component. Riccardo Leonardi et al integrated the theories of cyclic smoothness and wavelet analysis [18] and introduced a 3D wavelet-based method for cyclic analysis. D. Hanson et al combined cepstrum with the concept of cyclic cepstrum, applying it to modal analysis parameter identification through cyclic smooth analysis [19].…”

Section: Introductionmentioning

confidence: 99%

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Study on the Extraction Method for Track-Side Acoustic Features Based on Cyclic Stationary Analysis

Zhao,

Lu,

Chang

et al. 2023

Machines

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Because of its non-contact measurement characteristics, trackside acoustic technology is now utilized for train bearing fault diagnosis. However, the collected acoustic signal produces Doppler distortions that can impact the accuracy of bearing fault diagnosis. Additionally, when a fault occurs in the train bearing, it is analyzed using cyclostationary methods. In this study, we combine bearing fault characteristics with Doppler distortion correction and cyclostationary analysis methods. The trackside acoustic test platform is employed to collect and test the fault signals from bearings. These signals are processed and analyzed using Doppler distortion correction algorithms and cyclostationary techniques. A comparison between time domain maps and power spectrum maps before and after correction reveals an increase in SNR (signal to noise ratio) and a more concentrated energy distribution within the fault signals—at least a 50% improvement is observed. To further validate our method’s effectiveness, we select existing TADS equipment from a depot to collect bearing signals for analysis and processing using our proposed bearing fault diagnosis method. Comparison of time domain maps and power spectrum maps before and after correction shows clearer overall images and amplitude increase of nearly 125%. Therefore, we have successfully developed a stepwise method for bearing fault diagnosis based on cyclostationary Doppler distortion correction.

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Section: Magnitude Correctionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Study on the Extraction Method for Track-Side Acoustic Features Based on Cyclic Stationary Analysis

Zhao,

Lu,

Chang

et al. 2023

Machines

View full text Add to dashboard Cite

show abstract

“…Examples of the latter include hyperspectral [14] and volumetric medical [15] images. 3D data have been considered as well, e.g., in [16] the authors analyze the ciclostationary oscillations in a 3D wavelet coding framework. Such oscillations model the reconstruction error, which is due to the wavelet sub-bands quantization, with respect to the filter properties.…”

Section: Related Workmentioning

confidence: 99%

A wavelet filter comparison on multiple datasets for signal compression and denoising

Gnutti

Guerrini

Adami

et al. 2021

Multidim Syst Sign Process

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In this paper, we explicitly analyze the performance effects of several orthogonal and bi-orthogonal wavelet families. For each family, we explore the impact of the filter order (length) and the decomposition depth in the multiresolution representation. In particular, two contexts of use are examined: compression and denoising. In both cases, the experiments are carried out on a large dataset of different signal kinds, including various image sets and 1D signals (audio, electrocardiogram and seismic). Results for all the considered wavelets are shown on each dataset. Collectively, the study suggests that a meticulous choice of wavelet parameters significantly alters the performance of the above mentioned tasks. To the best of authors' knowledge, this work represents the most complete analysis and comparison between wavelet filters. Therefore, it represents a valuable benchmark for future works.

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On Bounds of Shift Variance in Two-Channel Multirate Filter Banks

Aach

Führ

2009

IEEE Trans. Signal Process.

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Cyclostationary error analysis and filter properties in a 3D wavelet coding framework

Cited by 5 publications

References 29 publications

Study on the Extraction Method for Track-Side Acoustic Features Based on Cyclic Stationary Analysis

Study on the Extraction Method for Track-Side Acoustic Features Based on Cyclic Stationary Analysis

A wavelet filter comparison on multiple datasets for signal compression and denoising

On Bounds of Shift Variance in Two-Channel Multirate Filter Banks

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