A Novel Weakly Matching Pursuit Recovery Algorithm and Its Application

Chen, Kai; Wei, Minxiang; Chen, Xinda; Pei, Yuhang; Li, Shunming

doi:10.1007/s42417-022-00443-w

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…The theory of CS points out that sparse or finite-dimensional signals can be reconstructed using measurements that are far less than the number of Nyquist sample points [12]. Due to the low sampling rate and high signal recovery accuracy, it has been widely used in multiple fields, such as single-pixel cameras [13], medical imaging [14], pattern recognition [15], and signal processing [16]. In the field of data restoration, Selenick et al [17] exploited the empirical wavelet transform with interpolation constraints to estimate block defects in images, which translated the estimation process into the l 1 norm.…”

Section: Introductionmentioning

confidence: 99%

A novel method to repair missing vibration data in rolling bearing vibration signals based on improved optimized measurement matrix

Wang

Liu

et al. 2023

Meas. Sci. Technol.

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Missing data caused by many factors such as equipment short circuits or data cleaning affect the accuracy of rotating machinery condition monitoring. To improve the precision of missing data recovery, a compressed sensing (CS)-based vibration data repair method is developed. Firstly, based on the Gaussian random matrix, an improved optimized measurement matrix (OMM) is proposed to accurately sample data. Then a sparse representation of the vibration signal through discrete cosine transform (DCT) is utilized to sparse the noisy vibration signal. Finally, the orthogonal matching pursuit (OMP) algorithm is employed to reconstruct the missing signal. The effectiveness of the proposed method is verified by analyzing constant and variable speed time series of rolling bearings. Compared with other data repair methods, it is highlighted that OMM has a higher repair precision at different loss rates.

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Section: Introductionmentioning

confidence: 99%

A novel method to repair missing vibration data in rolling bearing vibration signals based on improved optimized measurement matrix

Wang

Liu

et al. 2023

Meas. Sci. Technol.

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A fixed-time converging neurodynamic approach with time-varying coefficients for l1-minimization problem

Xu,

Li,

et al. 2024

Information Sciences

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Technique for Kernel Matching Pursuit Based on Intuitionistic Fuzzy c-Means Clustering

Lei,

Zhang

2024

Electronics

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Kernel matching pursuit (KMP) requires every step of the searching process to be global optimal searching in the redundant dictionary of functions in order to select the best matching signal structure. Namely, the dictionary learning time of KMP is too long. To solve the above drawbacks, a rough dataset was divided into some small-sized dictionaries to substitute local searching for global searching by using the property superiority of dynamic clustering performance, which is also superior in the intuitionistic fuzzy c-means (IFCM) algorithm. Then, we proposed a novel technique for KMP based on IFCM (IFCM-KMP). Subsequently, three tests including classification, effectiveness, and time complexity were carried out on four practical sample datasets, the conclusions of which fully demonstrate that the IFCM-KMP algorithm is superior to FCM and KMP.

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A Novel Weakly Matching Pursuit Recovery Algorithm and Its Application

Cited by 3 publications

References 36 publications

A novel method to repair missing vibration data in rolling bearing vibration signals based on improved optimized measurement matrix

A novel method to repair missing vibration data in rolling bearing vibration signals based on improved optimized measurement matrix

A fixed-time converging neurodynamic approach with time-varying coefficients for l1-minimization problem

Technique for Kernel Matching Pursuit Based on Intuitionistic Fuzzy c-Means Clustering

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