Compressed sensing based on dictionary learning for extracting impulse components

Chen, Xuefeng; Du, Zhaohui; Li, Jimeng; Li, Xiang; Zhang, Han

doi:10.1016/j.sigpro.2013.04.018

Cited by 160 publications

(62 citation statements)

References 67 publications

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“…(13) as sparse regularization. The sparse regularization model has been shown to have applications in image processing [25,26], signal reconstruction from incomplete measurements [23,25], impulse fault extraction [35] and more recently, sound source localization [27,28].…”

Section: Sparse Regularizationmentioning

confidence: 99%

Impact-force sparse reconstruction from highly incomplete and inaccurate measurements

Qiao

Zhang

Gao

2016

Journal of Sound and Vibration

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Section: Sparse Regularizationmentioning

confidence: 99%

Impact-force sparse reconstruction from highly incomplete and inaccurate measurements

Qiao

Zhang

Gao

2016

Journal of Sound and Vibration

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“…It then decided an estimate by taking the mean-square-error of the representations. Chen et al [52] proposed an adaptive dictionary learning method to extract impulse component from noisy environment. It first learned the sparse dictionary with the whole noisy signal and then searched impulse information with greedy algorithms from it.…”

Section: Signal Denoisingmentioning

confidence: 99%

Compressive sensing and sparse decomposition in precision machining process monitoring: From theory to applications

Zhu

Lin

et al. 2015

Mechatronics

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“…Tang et al used shift-invariant dictionary learning method to decompose the vibration signal into a series of latent components and then detected the bearing and gear faults [13]. Chen et al proposed a new method, namely, Sparse Extraction of Impulse by Adaptive Dictionary (SpaEIAD), to extract impulse components of vibration signals with heavy noise and detected the gearbox fault using the proposed method [14].…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Based on Improved Locality‐Constrained Linear Coding and Adaptive PSO‐Optimized SVM

Yuan

Chen

Dong

2017

Mathematical Problems in Engineering

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A novel bearing fault diagnosis method based on improved locality-constrained linear coding (LLC) and adaptive PSO-optimized support vector machine (SVM) is proposed. In traditional LLC, each feature is encoded by using a fixed number of bases without considering the distribution of the features and the weight of the bases. To address these problems, an improved LLC algorithm based on adaptive and weighted bases is proposed. Firstly, preliminary features are obtained by wavelet packet node energy. Then, dictionary learning with class-wise K-SVD algorithm is implemented. Subsequently, based on the learned dictionary the LLC codes can be solved using the improved LLC algorithm. Finally, SVM optimized by adaptive particle swarm optimization (PSO) is utilized to classify the discriminative LLC codes and thus bearing fault diagnosis is realized. In the dictionary leaning stage, other methods such as selecting the samples themselves as dictionary and -means are also conducted for comparison. The experiment results show that the LLC codes can effectively extract the bearing fault characteristics and the improved LLC outperforms traditional LLC. The dictionary learned by class-wise K-SVD achieves the best performance. Additionally, adaptive PSO-optimized SVM can greatly enhance the classification accuracy comparing with SVM using default parameters and linear SVM.

show abstract

Compressed sensing based on dictionary learning for extracting impulse components

Cited by 160 publications

References 67 publications

Impact-force sparse reconstruction from highly incomplete and inaccurate measurements

Impact-force sparse reconstruction from highly incomplete and inaccurate measurements

Compressive sensing and sparse decomposition in precision machining process monitoring: From theory to applications

Bearing Fault Diagnosis Based on Improved Locality‐Constrained Linear Coding and Adaptive PSO‐Optimized SVM

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