Quantitative fault analysis of roller bearings based on a novel matching pursuit method with a new step-impulse dictionary

Cui, Lingli; Wu, Na; Ma, Chunqing; Wang, Huaqing

doi:10.1016/j.ymssp.2015.05.032

Cited by 108 publications

(47 citation statements)

References 17 publications

(20 reference statements)

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“…The results showed that it is effective to extract vibration features. Cui et al [40] developed a matching pursuit algorithm based on dictionary step-impulse to detect the rolling bearing spalling size. First, the step response into the spalling region and the excitation response out the spalling region is obtained, then the relationship between the responses, spalling size and time interval is calculated.…”

Section: Fig 2 Pattern Recognition Curve For Various Window Sizes[21]mentioning

confidence: 99%

Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review

Duan

Guo

et al. 2018

Int J Adv Manuf Technol

162

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A rolling bearing is an essential component of a rotating mechanical transmission system. Its performance and quality directly affects the life and reliability of machinery. Bearings' performance and reliability need high requirements because of a more complex and poor working conditions of bearings. A bearing with high reliability reduces equipment operation accidents, equipment maintenance costs and achieves condition-based maintenance. First in this paper, the development of technology of the main individual physical condition monitoring and fault diagnosis of rolling bearings are introduced, then the fault diagnosis technology of multi-sensors information fusion is introduced, finally the advantages, disadvantages and trends developed in the future of the detection main individual physics technology and multi-sensors information fusion technology are summarized. This paper is expected to provide the necessary basis for the follow-up study of the fault diagnosis of rolling bearings and a foundational knowledge for researchers about rolling bearings.

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Section: Fig 2 Pattern Recognition Curve For Various Window Sizes[21]mentioning

confidence: 99%

Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review

Duan

Guo

et al. 2018

Int J Adv Manuf Technol

162

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“…13,14 Matching pursuit (MP) is the most commonly used sparse decomposition method, and searches the over-complete atom library to realize signal's projection on the optimal atom. 15,16 Previous studies have shown that multiple characteristics and clustering algorithms can be used to identify strong interference in MT/AMT signals. 17 Although these methods have achieved better signal-noise identification effects, it is still necessary to excavate more characteristics to describe the complexity of signal, and it takes some time to calculate characteristic parameters.…”

Section: Introductionmentioning

confidence: 99%

Audio Magnetotelluric Signal-Noise Identification and Separation Based on Multifractal Spectrum and Matching Pursuit

Zhang

Tang

et al. 2019

Fractals

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To avoid the blindness of the overall de-noising method and retain useful low frequency signals that are not over processed, we proposed a novel audio magnetotelluric (AMT) signal-noise identification and separation method based on multifractal spectrum and matching pursuit. We extracted two sets of multifractal spectrum characteristic from AMT time-series data to analyze the singularity. We used a support vector machine approach to learn the multifractal spectrum characteristics in a sample’s library and generate a model of support vector machine to distinguish between sections with and without interference in the measured AMT data. The matching pursuit algorithm was used to separate only those sections identified as having interference. Experimental results showed that the proposed method can effectively identify interference in the EMTF mathematical model and measured AMT data. Sections without interference were accurately preserved and reconstructed AMT signals were close to the natural electromagnetic field. The resulting apparent resistivity-phase curve is more continuous and smooth, and effectively improves the quality of AMT data. Moreover, the proposed method provides more reliable AMT data for subsequent electromagnetic inversion.

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“…Reliable condition monitoring techniques are essential when performing condition-based maintenance on expensive rotating machine assets [1,2]. Advanced signal processing [3][4][5][6][7][8][9][10][11][12][13] and sophisticated supervised machine learning techniques [14][15][16][17][18][19][20][21][22][23] are actively investigated to improve the condition monitoring task. Deep learning techniques have also recently been used to not only infer the condition of the machine, but also to extract features from the raw dataset i.e.…”

Section: Introductionmentioning

confidence: 99%

An open set recognition methodology utilising discrepancy analysis for gear diagnostics under varying operating conditions

Schmidt

Heyns

2019

Mechanical Systems and Signal Processing

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Quantitative fault analysis of roller bearings based on a novel matching pursuit method with a new step-impulse dictionary

Cited by 108 publications

References 17 publications

Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review

Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review

Audio Magnetotelluric Signal-Noise Identification and Separation Based on Multifractal Spectrum and Matching Pursuit

An open set recognition methodology utilising discrepancy analysis for gear diagnostics under varying operating conditions

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