Sparse signal recovery with multiple prior information: Algorithm and measurement bounds

Luong, Huynh Van; Deligiannis, Nikos; Seiler, Jurgen; Forchhammer, Søren; Kaup, André

doi:10.1016/j.sigpro.2018.06.019

Cited by 3 publications

(1 citation statement)

References 54 publications

(152 reference statements)

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“…So here, multiple prior signals method can replace the one prior signal, which can further reconstruct a spare signal, so the reconstruction algorithm with the multiple side information signal (RAMSI) based on the CS with weighing extension was proposed. The RAMSI algorithm takes advantage from the side information signals to reconstruct signals [19,20].…”

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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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.

View full text Add to dashboard Cite

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Smoothing Modified Newton Algorithm Based on L_pNorm Regularization for Signal Recovery

Yang

Zhang

et al. 2020

2020 15th IEEE International Conference on Signal Processing (ICSP)

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An Approximate Message Passing Framework for Side Information

Rush

Baron

et al. 2019

IEEE Trans. Signal Process.

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Approximate message passing (AMP) methods have gained recent traction in sparse signal recovery. Additional information about the signal, or side information (SI), is commonly available and can aid in efficient signal recovery. This work presents an AMP-based framework that exploits SI and can be readily implemented in various settings for which the SI results in separable distributions. To illustrate the simplicity and applicability of our approach, this framework is applied to a Bernoulli-Gaussian (BG) model and a time-varying birth-death-drift (BDD) signal model, motivated by applications in channel estimation. We develop a suite of algorithms, called AMP-SI, and derive denoisers for the BDD and BG models. Numerical evidence demonstrating the advantages of our approach are presented alongside empirical evidence of the accuracy of a proposed state evolution.

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Sparse signal recovery with multiple prior information: Algorithm and measurement bounds

Cited by 3 publications

References 54 publications

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

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

Smoothing Modified Newton Algorithm Based on L_pNorm Regularization for Signal Recovery

An Approximate Message Passing Framework for Side Information

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Sparse signal recovery with multiple prior information: Algorithm and measurement bounds

Cited by 3 publications

References 54 publications

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

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

Smoothing Modified Newton Algorithm Based on LpNorm Regularization for Signal Recovery

An Approximate Message Passing Framework for Side Information

Contact Info

Product

Resources

About

Smoothing Modified Newton Algorithm Based on L_pNorm Regularization for Signal Recovery