G
Gaigai Cai
Researcher at Soochow University (Suzhou)
Publications - 35
Citations - 1544
Gaigai Cai is an academic researcher from Soochow University (Suzhou). The author has contributed to research in topics: Sparse approximation & Feature extraction. The author has an hindex of 15, co-authored 34 publications receiving 1136 citations. Previous affiliations of Gaigai Cai include Xidian University & Xi'an Jiaotong University.
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Matching Demodulation Transform and SynchroSqueezing in Time-Frequency Analysis
TL;DR: The authors introduce an iterative algorithm, called matching demodulation transform (MDT), to generate a time-frequency (TF) representation with satisfactory energy concentration, and the MDT-based synchrosqueezing algorithm is described to further enhance the concentration and reduce the diffusion of the curved IF profile in the TF representation of original syn chrosquEEzing transform.
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Sparsity-enabled signal decomposition using tunable Q-factor wavelet transform for fault feature extraction of gearbox
TL;DR: In this paper, a sparsity-enabled signal decomposition method was proposed to extract fault features of gearboxes by analyzing the oscillatory behavior of the signal rather than the frequency or scale.
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Nonconvex Sparse Regularization and Convex Optimization for Bearing Fault Diagnosis
TL;DR: A nonconvex sparse regularization method for bearing fault diagnosis is proposed based on the generalized minimax-concave (GMC) penalty, which maintains the convexity of the sparsity-regularized least squares cost function, and thus the global minimum can be solved by convex optimization algorithms.
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An automatic and robust features learning method for rotating machinery fault diagnosis based on contractive autoencoder
TL;DR: A method based on stacked CAE for automatic robust features extraction and fault diagnosis of rotating machinery is proposed and results show that the diagnosis accuracies of the proposed method are higher than those of the stacked autoencoder (AE) network under each SNR.
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Reliability estimation for cutting tools based on logistic regression model using vibration signals
TL;DR: In this article, a reliability estimation approach to the cutting tools based on logistic regression model by using vibration signals has been proposed, which does not require any assumption about degradation paths and probability density functions of condition parameters.