A signal processing approach to modeled bearing faults detection in electric system
Author:
Affiliation:
1. University of Boumerdes, LMSS Laboratory,Boumerdes,Algeria
2. University of Boumerdes, LEMI Laboratory,Boumerdes,Algeria
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9955493/9955494/09955946.pdf?arnumber=9955946
Reference17 articles.
1. Distributed fault diagnosis of rotor-bearing system using dimensional analysis and experimental methods;prakash;Measurement,2020
2. Novel self-adaptive vibration signal analysis: Concealed component decomposition and its application in bearing fault diagnosis
3. Auto correlated Envelopes for early fault detection of rolling bearings;yuandong;Mechanical Systems and Signal Processing,2021
4. Rolling Bearing Failure Detection in Induction Motors using Stator Current, Vibration and Stray Flux Analysis Techniques
5. Time-Frequency Analysis Based on Minimum-Norm Spectral Estimation to Detect Induction Motor Faults
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1. Micro-Grids Integration Using Remote Synchronisation Based on Micro-PMU;2024 2nd International Conference on Electrical Engineering and Automatic Control (ICEEAC);2024-05-12
2. Advancing bearing fault diagnosis under variable working conditions: a CEEMDAN-SBS approach with vibro-electric signal integration;The International Journal of Advanced Manufacturing Technology;2024-04-03
3. Advancing Bearing Fault Diagnosis under Variable Working Conditions: A CEEMDAN-SBS Approach with Vibro-Electric Signal Integration;2023-12-29
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