Bearing Faults Detection Using Statistical Feature Extraction and Probability Based Distance: A Comparative Study
Author:
Affiliation:
1. Université Paris Saclay,CNRS, CentraleSupelec, Laboratoire des Signaux et Systèmes UMR 8506,Gif Sur Yvette,France
Funder
China Scholarship Council
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9968313/9968303/09968305.pdf?arnumber=9968305
Reference17 articles.
1. Multisensor Fault Detection and Isolation Using Kullback Leibler Divergence: Application to Data Vibration Signals
2. Detection Capability For Incipient Faults in a Noisy Environment Using PDF and CDF Based Techniques: a Comparative Study
3. An adaptive fault detection and root-cause analysis scheme for complex industrial processes using moving window KPCA and information geometric causal inference
4. An incipient fault diagnosis methodology using local Mahalanobis distance: Detection process based on empirical probability density estimation
5. An incipient fault diagnosis methodology using local Mahalanobis distance: Fault isolation and fault severity estimation
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