Maolin Li
Xi'an Jiaotong University
12 Papers
16 Citations
Maolin Li is an academic researcher from Xi'an Jiaotong University. The author has contributed to research in topics: Feature extraction & Time–frequency analysis. The author has an hindex of 4, co-authored 11 publications.
Chat about Author
Papers
Feature selection for machine fault diagnosis using clustering of non-negation matrix factorization
TL;DR: A novel feature selection method based on the non-negation matrix factorization (NMF) based on Alternating Least Squares (ALS) algorithm with sparsity control and decorrelation constrains to factorize original feature space into two low-rank matrixes (projection vectors and feature spaces).
35
Application of Instantaneous Rotational Speed to Detect Gearbox Faults Based on Double Encoders
TL;DR: An alternative gearbox fault detection method based on the instantaneous rotational speed is proposed because of its advantages over vibration analysis and it is proved that localized faults in the gearbox generate small angular speed fluctuations, which are measurable with an optical encoder.
Manifold Learning with Self-Organizing Mapping for Feature Extraction of Nonlinear Faults in Rotating Machinery
TL;DR: In this article, a new method for extracting the low-dimensional feature automatically with self-organization mapping manifold is proposed for the detection of rotating mechanical nonlinear faults (such as rubbing, pedestal looseness).
Feature Extraction of Impulse Faults for Vibration Signals Based on Sparse Non-Negative Tensor Factorization
TL;DR: Wang et al. as mentioned in this paper proposed a novel method of impulse feature extraction for vibration signals, based on sparse non-negative tensor factorization, which is suitable for matrix processing but challenged by the higher-order data.
10
Impulse Feature Extraction of Bearing Faults Based on Convolutive Nonnegative Matrix Factorization
TL;DR: Both numerical simulation and experimental verifications on bearings indicate that the proposed impulse feature extraction method can eliminate the influence of random shock excitations and directly attain the periodic impulse for the source of the bearing fault, and that its extraction effectiveness outperforms MOMEDA.
8