An automatic and parameter-free information-based method for sparse representation in wavelet bases

2020 ◽  
Vol 176 ◽  
pp. 73-95 ◽  
Author(s):  
V. Bruni ◽  
L. Della Cioppa ◽  
D. Vitulano
Keyword(s):  
Sparse Representation ◽  
Wavelet Bases ◽  
Free Information

scholarly journals Research on the Sparse Representation for Gearbox Compound Fault Features Using Wavelet Bases

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Chunyan Luo ◽  
Changqing Shen ◽  
Wei Fan ◽  
Gaigai Cai ◽  
Weiguo Huang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Sparse Representation ◽  
Background Noise ◽  
Practical Application ◽  
Impulse Responses ◽  
Wavelet Bases ◽  
Potential Approach ◽  
Fault Features ◽  
Engineering Practices

The research on gearbox fault diagnosis has been gaining increasing attention in recent years, especially on single fault diagnosis. In engineering practices, there is always more than one fault in the gearbox, which is demonstrated as compound fault. Hence, it is equally important for gearbox compound fault diagnosis. Both bearing and gear faults in the gearbox tend to result in different kinds of transient impulse responses in the captured signal and thus it is necessary to propose a potential approach for compound fault diagnosis. Sparse representation is one of the effective methods for feature extraction from strong background noise. Therefore, sparse representation under wavelet bases for compound fault features extraction is developed in this paper. With the proposed method, the different transient features of both bearing and gear can be separated and extracted. Both the simulated study and the practical application in the gearbox with compound fault verify the effectiveness of the proposed method.

Prediction method for outliers over data stream based on sparse representation

2010 ◽  
Vol 30 (11) ◽  
pp. 2956-2958
Author(s):  
Xue-song XU ◽  
Ling-juan LI ◽  
Li-wei GUO
Keyword(s):  
Sparse Representation ◽  
Data Stream ◽  
Prediction Method

Ultrasonic Block Compressed Sensing Imaging Reconstruction Algorithm Based on Wavelet Sparse Representation

2020 ◽  
Vol 16 (3) ◽  
pp. 262-272
Author(s):  
Guangzhi Dai ◽  
Zhiyong He ◽  
Hongwei Sun
Keyword(s):  
Compressed Sensing ◽  
Sparse Representation ◽  
Linear Array ◽  
Imaging System ◽  
Ultrasonic Imaging ◽  
Large Data ◽  
Reconstruction Algorithm ◽  
Array Transducer ◽  
Total Data ◽  
Block Compressed Sensing

Background: This study is carried out targeting the problem of slow response time and performance degradation of imaging system caused by large data of medical ultrasonic imaging. In view of the advantages of CS, it is applied to medical ultrasonic imaging to solve the above problems. Objective: Under the condition of satisfying the speed of ultrasound imaging, the quality of imaging can be further improved to provide the basis for accurate medical diagnosis. Methods: According to CS theory and the characteristics of the array ultrasonic imaging system, block compressed sensing ultrasonic imaging algorithm is proposed based on wavelet sparse representation. Results: Three kinds of observation matrices have been designed on the basis of the proposed algorithm, which can be selected to reduce the number of the linear array channels and the complexity of the ultrasonic imaging system to some extent. Conclusion: The corresponding simulation program is designed, and the result shows that this algorithm can greatly reduce the total data amount required by imaging and the number of data channels required for linear array transducer to receive data. The imaging effect has been greatly improved compared with that of the spatial frequency domain sparse algorithm.

Sparse Representation Based Dielectric Loss Angle Measurement

2015 ◽  
Vol 9 (1) ◽  
pp. 566-570
Author(s):  
Zhang Ji ◽  
Jianfeng Zheng
Keyword(s):  
Dielectric Loss ◽  
Power Systems ◽  
Sparse Representation ◽  
Precise Measurement ◽  
Angle Measurement ◽  
Measuring Error ◽  
Loss Angle ◽  
Analysis Method ◽  
Electric Capacity ◽  
Dielectric Loss Angle

Precise measurement of dielectric loss angle is very important for electric capacity equipment in recent power systems. When signal-to-noise is low and fundamental frequency is fluctuating, aiming at the measuring error of dielectric loss angle based on some recent Fourier transform and wavelet transform harmonics analysis method, we propose a novel algorithm based on sparse representation, and improved it to be more flexible for signal sampling. Comparison experiments describe the advantages of our method.

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