Study on Incipient Fault Diagnosis for Rolling Bearings Based on Wavelet and Neural Networks

2008 ◽  
Author(s):  
Yuzhong Li
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Rolling Bearings ◽  
Incipient Fault ◽  
Incipient Fault Diagnosis

scholarly journals Correction: Li, Q.; Liang, S.Y. Incipient Fault Diagnosis of Rolling Bearings Based on Impulse-Step Impact Dictionary and Re-Weighted Minimizing Nonconvex Penalty Lq Regular Technique. Entropy 2017, 19, 421

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 483
Author(s):  
Qing Li ◽  
Steven Y. Liang
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearings ◽  
Incipient Fault ◽  
Nonconvex Penalty ◽  
Incipient Fault Diagnosis ◽  
Made In

The authors were not aware of some errors and imprecise descriptions made in the proofreading phase, therefore, we wish to make the following corrections to this paper [...]

Incipient fault diagnosis of chemical processes via artificial neural networks

AIChE Journal ◽  
1989 ◽  
Vol 35 (11) ◽  
pp. 1803-1812 ◽  
Author(s):  
Kajiro Watanabe ◽  
Ichiro Matsuura ◽  
Masahiro Abe ◽  
Makoto Kubota ◽  
D. M. Himmelblau
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Diagnosis ◽  
Chemical Processes ◽  
Incipient Fault ◽  
Artificial Neural ◽  
Incipient Fault Diagnosis

scholarly journals Incipient Fault Diagnosis of Rolling Bearings Based on Impulse-Step Impact Dictionary and Re-Weighted Minimizing Nonconvex Penalty Lq Regular Technique

Entropy ◽  
2017 ◽  
Vol 19 (8) ◽  
pp. 421 ◽  
Author(s):  
Qing Li ◽  
Steven Liang
Keyword(s):  
Fault Diagnosis ◽  
Matching Pursuit ◽  
Vibration Signal ◽  
L1 Norm ◽  
Rolling Bearings ◽  
Mode Decomposition ◽  
Incipient Fault ◽  
Nonconvex Penalty ◽  
Treat All ◽  
Incipient Fault Diagnosis

The periodical transient impulses caused by localized faults are sensitive and important characteristic information for rotating machinery fault diagnosis. However, it is very difficult to accurately extract transient impulses at the incipient fault stage because the fault impulse features are rather weak and always corrupted by heavy background noise. In this paper, a new transient impulse extraction methodology is proposed based on impulse-step dictionary and re-weighted minimizing nonconvex penalty Lq regular (R-WMNPLq, q = 0.5) for the incipient fault diagnosis of rolling bearings. Prior to the sparse representation, the original vibration signal is preprocessed by the variational mode decomposition (VMD) technique. Due to the physical mechanism of periodic double impacts, including step-like and impulse-like impacts, an impulse-step impact dictionary atom could be designed to match the natural waveform structure of vibration signals. On the other hand, the traditional sparse reconstruction approaches such as orthogonal matching pursuit (OMP), L1-norm regularization treat all vibration signal values equally and thus ignore the fact that the vibration peak value may have more useful information about periodical transient impulses and should be preserved at a larger weight value. Therefore, penalty and smoothing parameters are introduced on the reconstructed model to guarantee the reasonable distribution consistence of peak vibration values. Lastly, the proposed technique is applied to accelerated lifetime testing of rolling bearings, where it achieves a more noticeable and higher diagnostic accuracy compared with OMP, L1-norm regularization and traditional spectral Kurtogram (SK) method.

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