Robust enhanced trend filtering with unknown noise

Zhao, Zhibin; Wang, Shibin; Wong, David C.; Sun, Chuang; Yan, Ruqiang; Chen, Xuefeng

doi:10.1016/j.sigpro.2020.107889

Cited by 10 publications

(3 citation statements)

References 39 publications

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“…Moreover, in most existing studies, the sparsity of the coefficients is achieved by an optimization equation with l 1 -norm regularization, which can induce sparsity better. However, it usually underestimates the high amplitude components of the signal and suffers from a lack of accuracy in estimating the fault impulse components [27], which most likely leads to an incorrect diagnosis, thus reducing the utilization of powerful STFR. Several scholars have conducted related studies on the penalty function.…”

Section: Introductionmentioning

confidence: 99%

Rolling bearing incipient fault feature extraction using impulse-enhanced sparse time-frequency representation

Zhu

Jiang

Yao

et al. 2023

Meas. Sci. Technol.

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Incipient faults features are often extremely weak and susceptible to heavy noise, making it challenging to obtain the concentrated faulty energy ridges in the time-frequency domain. Thus, a novel impulse-enhanced sparse time-frequency representation (IESTFR) method is proposed in this paper. First, the time-rearranged multisynchrosqueezing transform is utilized to produce a time-frequency representation with a high energy concentration for faulty impulses. Next, a new non-convex penalty function is constructed by the hyperbolic tangent function, which can enhance the periodic impulsivity of sparse time-frequency representation for more obvious fault characteristic frequency. Moreover, the time-frequency transform is evaluated and compared by simulated signals and a selection strategy for the regularization parameter is designed. Simulated signals and two experimental signals are applied to verify the effectiveness of IESTFR, and the results show that IESTFR is effective and superior in bearing incipient fault feature extraction.

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Section: Introductionmentioning

confidence: 99%

Rolling bearing incipient fault feature extraction using impulse-enhanced sparse time-frequency representation

Zhu

Jiang

Yao

et al. 2023

Meas. Sci. Technol.

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“…Background issues are commonly solved using a host of filling, fitting and filtering methods. We refer to overviews in [5], [6], and for AC to background corrections backcor [7] and BEADS [8].…”

Section: Introductionmentioning

confidence: 99%

PENDANTSS: PEnalized Norm-ratios Disentangling Additive Noise, Trend and Sparse Spikes

Zheng,

Chouzenoux,

Duval

2023

Preprint

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Denoising, detrending, deconvolution: usual restoration tasks, traditionally decoupled. Coupled formulations entail complex ill-posed inverse problems. We propose PENDANTSS for joint trend removal and blind deconvolution of sparse peaklike signals. It blends a parsimonious prior with the hypothesis that smooth trend and noise can somewhat be separated by low-pass filtering. We combine the generalized quasi-norm ratio SOOT/SPOQ sparse penalties p/ q with the BEADS ternary assisted source separation algorithm. This results in a both convergent and efficient tool, with a novel Trust-Region block alternating variable metric forward-backward approach. It outperforms comparable methods, when applied to typically peaked analytical chemistry signals. Reproducible code is provided.

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“…However, the obtained real-life signals are often corrupted by various types of noise, which occur due to numerous environmental factors and to the data acquisition and transmission processes themselves. Therefore, prior to the further exploitation and analysis of such data, it needs to be processed by filtering algorithms to reduce the noise, thus enabling more efficient reconstruction of the original information content [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Particle-Swarm-Optimization-Enhanced Radial-Basis-Function-Kernel-Based Adaptive Filtering Applied to Maritime Data

Lopac

Jurdana

Lerga

et al. 2021

JMSE

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The real-life signals captured by different measurement systems (such as modern maritime transport characterized by challenging and varying operating conditions) are often subject to various types of noise and other external factors in the data collection and transmission processes. Therefore, the filtering algorithms are required to reduce the noise level in measured signals, thus enabling more efficient extraction of useful information. This paper proposes a locally-adaptive filtering algorithm based on the radial basis function (RBF) kernel smoother with variable width. The kernel width is calculated using the asymmetrical combined-window relative intersection of confidence intervals (RICI) algorithm, whose parameters are adjusted by applying the particle swarm optimization (PSO) based procedure. The proposed RBF-RICI algorithm’s filtering performances are analyzed on several simulated, synthetic noisy signals, showing its efficiency in noise suppression and filtering error reduction. Moreover, compared to the competing filtering algorithms, the proposed algorithm provides better or competitive filtering performance in most considered test cases. Finally, the proposed algorithm is applied to the noisy measured maritime data, proving to be a possible solution for a successful practical application in data filtering in maritime transport and other sectors.

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Robust enhanced trend filtering with unknown noise

Cited by 10 publications

References 39 publications

Rolling bearing incipient fault feature extraction using impulse-enhanced sparse time-frequency representation

Rolling bearing incipient fault feature extraction using impulse-enhanced sparse time-frequency representation

PENDANTSS: PEnalized Norm-ratios Disentangling Additive Noise, Trend and Sparse Spikes

Particle-Swarm-Optimization-Enhanced Radial-Basis-Function-Kernel-Based Adaptive Filtering Applied to Maritime Data

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