Multi‐band joint local sparse tracking via wavelet transforms

Han, Guang; Luo, Heng; Liu, Jixin; Sun, Ning; Du, Kun; Li, Xiaofei

doi:10.1049/iet-cvi.2016.0079

Cited by 5 publications

(3 citation statements)

References 42 publications

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“…Thus it becomes a key problem whether and where fault features will appear and how they vary from time for bearing degradation detection and performance assessment. In [2] and [32], spectral structure and sub-bands are tracked to monitor and determine the fault characteristic frequency based on the spectrum and time frequency image.…”

Section: Preliminary a Statistical Distribution Of Vibration Signalmentioning

confidence: 99%

Rolling Bearing Incipient Degradation Monitoring and Performance Assessment Based on Signal Component Tracking

et al. 2019

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To ensure a long-time stable operation of the rolling bearing, it is important to accurately assess their working performance, especially the incipient degradation based on the massive service process data. As a new and effective tool, deep learning model is applied widely in the field of fault diagnosis but limited to rare labeled data. In this paper, a bearing performance assessment method based on signal component tracking is proposed to realize the bearing degradation detection. More general features are obtained by local convolution operation to represent the local characteristics in the spectrum or time-frequency distribution of vibration signal, which follows the forward features mapping process of the convolutional neural network (CNN). Then, a novel quantification criterion based on the comparison of those local features is used to provide the selection strategy of optimal fault components. The proposed method takes into account the abnormal information in degradation monitoring and utilizes it to achieve bearing incipient fault diagnosis. The experimental results prove that the features extracted by the proposed method possess high recognition efficiency when being used in incipient failure detection and diagnosis. INDEX TERMS Local feature, incipient fault diagnosis, performance assessment. I. INTRODUCTION

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Section: Preliminary a Statistical Distribution Of Vibration Signalmentioning

confidence: 99%

Rolling Bearing Incipient Degradation Monitoring and Performance Assessment Based on Signal Component Tracking

et al. 2019

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“…Typical constraints include sparsity of the spectrum [12][13][14][15][16] and low rankness of time-domain data, which is called the free induction decay (FID) signal, of the spectrum [17][18][19][20][21][22]. The former assumes that non-zero values are rare, for example, sparsity [12][13][14][15][16], in the spectrum and explores the l 1 norm [23][24][25] to constrain spectral sparsity. Although narrow peaks can be reconstructed very well, reconstruction of broad peaks may be compromised since they violate the assumption of sparsity.…”

Section: Introductionmentioning

confidence: 99%

A partial sum of singular‐value‐based reconstruction method for non‐uniformly sampled NMR spectroscopy

Wang

Zhan

et al. 2020

IET Signal Processing

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The nuclear magnetic resonance (NMR) spectroscopy has fruitful applications in chemistry, biology and life sciences, but suffers from long acquisition time. Non-uniform sampling is a typical fast NMR method by undersampling the time-domain data of the spectrum but need to restore the fully sampled data with proper constraints. The state-ofthe-art method is to model the time-domain data as the sum of exponential functions and reconstruct these data by enforcing the low rankness of Hankel matrix. However, this method is solved by minimizing the sum of singular values of the Hankel matrix, which leads to the distortion of low-intensity spectral peaks. Here, a low rank Hankel matrix reconstruction approach with a partial sum of singular values is proposed to protect small singular values, which can faithfully reconstruct all peaks. Results on both synthetic and realistic NMR spectroscopy show that the proposed method can reconstruct a more consistent spectrum to the fully sampled one than other state-of-the-art methods and have particular advantages on preserving low-intensity peaks. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Methods [8][9][10] took partial and spatial information into consideration to exploit more robust templates with massive burdens about atom dictionary construction and pooling calculation procedures. Even wavelet transformation-based features were extracted to improve the reliability of appearance modeling where joint dictionaries for sparse coding still required tough storage procedures [11]. Intuitively, discriminative method provides adaptive complementary option for various appearance changes [1].…”

Section: Introductionmentioning

confidence: 99%

Optimal Metric Evaluation-Based Multicue Inverse Sparse Appearance Model for Object Tracking

Zhao

Sun

et al. 2020

Mathematical Problems in Engineering

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In order to obtain the discriminative compact appearance model for tracking objects effectively, this paper proposes a new structural tracking strategy that includes multicue inverse sparse appearance model and optimal metric evaluation between online robust templates and a limited number of particle samples in the looping process. Multicue inverse sparse appearance model globally improves the efficient selection of informative particle samples that can avoid the cumbersome coding and decoding cost for the trivial random particle samples. Only the most potential crucial cases are involved in each tracking loop. This refrains from unreasonable, rough numerical reduction of particle samples and also keeps the unbiasedness and dynamic stochasticness of the sampling process. Meanwhile, low-rank self-representatives for positive and negative samples facilitate the formulation of a suitable code book that arranges the useful sparse coefficients for feature bags and facilitates optimal metric evaluation for online training. It also alleviates the accuracy degradation of tracking occluded objects and improves the robustness of the tracker. Both of them preserve the discriminative compactness of target which speeds up particle filtering localization to separate the target object from distractors. Moreover, the proposed method exploits online appearance representations to learn the sharing compact information that avoids massive calculation burdens for massive visual data.

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Multi‐band joint local sparse tracking via wavelet transforms

Cited by 5 publications

References 42 publications

Rolling Bearing Incipient Degradation Monitoring and Performance Assessment Based on Signal Component Tracking

Rolling Bearing Incipient Degradation Monitoring and Performance Assessment Based on Signal Component Tracking

A partial sum of singular‐value‐based reconstruction method for non‐uniformly sampled NMR spectroscopy

Optimal Metric Evaluation-Based Multicue Inverse Sparse Appearance Model for Object Tracking

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