Fafa Chen
Chongqing University
7 Papers
Fafa Chen is an academic researcher from Chongqing University. The author has contributed to research in topics: Computer science & Support vector machine. The author has an hindex of 2, co-authored 2 publications.
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Papers
A novel fault diagnosis model for gearbox based on wavelet support vector machine with immune genetic algorithm
TL;DR: The experimental results indicate that this proposed approach is an effective method for gearbox fault diagnosis, which has more strong generalization ability and can achieve higher diagnostic accuracy than that of the artificial neural network and the SVM which has randomly extracted parameters.
155
Multi-fault diagnosis study on roller bearing based on multi-kernel support vector machine with chaotic particle swarm optimization
TL;DR: The experimental results indicate that this proposed approach is an effective method for roller bearing fault diagnosis, which has more strong generalization ability and can achieve higher diagnostic accuracy than that of the single kernel SVM or the MSVM which parameters are randomly extracted.
126
Integrated early fault diagnosis method based on direct fast iterative filtering decomposition and effective weighted sparseness kurtosis to rolling bearings
TL;DR: In this paper , a Direct Fast Iterative Filtering (dFIF) method was proposed for feature extraction of bearing fault diagnosis signals, which is used to quickly decompose multi-component signals into a set of intrinsic mode functions by means of fast Fourier transform (FFT), and the Hilbert envelope demodulation analysis was used to extract the bearing fault feature and then judge the fault type.
35
A time-varying instantaneous frequency fault features extraction method of rolling bearing under variable speed
Baojia Chen,Zhichao Hai,Xueliang Chen,Fafa Chen,Wenrong Xiao,Neng Qi Xiao,Wenlong Fu,Qiang Liu,Zhuxin Tian,Gongfa Li +9 more
TL;DR: Wang et al. as discussed by the authors proposed a time-varying instantaneous frequency fault features extraction method of rolling bearing under variable speed, which combined with the improved multisynchrosqueezing transform (IMSST), empirical Fourier decomposition (EFD), and generalized demodulation (GD).
11
Reliability Assessment Method Based on Condition Information by Using Improved Proportional Covariate Model
TL;DR: In this paper , an improved proportional covariate model (IPCM) is put forward based on the logistic regression model (LRM), where the salient features reflecting the equipment degradation process are extracted from the existing monitoring signals, which are considered as the input of the LRM.
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