Asymmetric penalty sparse model based cepstrum analysis for bearing fault detections

2020 ◽  
Vol 165 ◽  
pp. 107288 ◽  
Author(s):  
Yi Liu ◽  
Zhansi Jiang ◽  
Huang Haizhou ◽  
Jiawei Xiang
Keyword(s):  
Sparse Model ◽  
Bearing Fault ◽  
Model Based ◽  
Cepstrum Analysis

scholarly journals Vertex finding by sparse model-based clustering

2016 ◽  
Vol 762 ◽  
pp. 012055
Author(s):  
R Frühwirth ◽  
K Eckstein ◽  
S Frühwirth-Schnatter
Keyword(s):  
Model Based Clustering ◽  
Sparse Model ◽  
Model Based

A Bearing Fault Diagnosis Model Based on Deformable Atrous Convolution and Squeeze-and-Excitation Aggregation

2021 ◽  
Vol 70 ◽  
pp. 1-10
Author(s):  
Yang Wang ◽  
Miaomiao Yang ◽  
Yupeng Zhang ◽  
Zeda Xu ◽  
Jigang Huang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Model Based ◽  
Diagnosis Model

scholarly journals Rapid 3D dynamic arterial spin labeling with a sparse model-based image reconstruction

NeuroImage ◽  
2015 ◽  
Vol 121 ◽  
pp. 205-216 ◽  
Author(s):  
Li Zhao ◽  
Samuel W. Fielden ◽  
Xue Feng ◽  
Max Wintermark ◽  
John P. Mugler ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Arterial Spin Labeling ◽  
Spin Labeling ◽  
Sparse Model ◽  
Model Based

Fusion of Sparse Model Based on Randomly Erased Image for SAR Occluded Target Recognition

2020 ◽  
Vol 58 (11) ◽  
pp. 7829-7844
Author(s):  
Zhiqiang He ◽  
Huaitie Xiao ◽  
Chao Gao ◽  
Zhuangzhuang Tian ◽  
Si-Wei Chen
Keyword(s):  
Target Recognition ◽  
Sparse Model ◽  
Model Based

A Transformer Model-Based Approach to Bearing Fault Diagnosis

2021 ◽  
pp. 65-79
Author(s):  
Zhenshan Bao ◽  
Jialei Du ◽  
Wenbo Zhang ◽  
Jiajing Wang ◽  
Tao Qiu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Model Based ◽  
Transformer Model

Mutual Information Based Feature Selection From Data Driven and Model Based Techniques for Fault Detection in Rolling Element Bearings

2011 ◽  
Author(s):  
Karthik Kappaganthu ◽  
C. Nataraj
Keyword(s):  
Fault Detection ◽  
Mutual Information ◽  
Diagnostic System ◽  
Classification Performance ◽  
Data Driven ◽  
Time Frequency ◽  
Bearing Fault ◽  
Model Based ◽  
Rolling Element ◽  
And Performance

This paper proposes a novel technique combining datadriven and model-based techniques to significantly improve the performance in bearing fault diagnostics. Features that provide best classification performance for the given data are selected from a combined set of data driven and model based features. Some of the common data driven techniques from time, frequency and time-frequency domain are considered. For model based feature extraction, recently developed cross-sample entropy is used. The ranking and performance of each of these feature sets are studied, when used independently and when used together. Mutual information based technique is used for ranking and selection of the optimal feature set. Using this method, the contribution to performance and redundancy of each of the data driven features and model based features can be studied. This method can be used to design an effective diagnostic system for bearing fault detection.

Sign in / Sign up

Export Citation Format

Share Document