Diagnosis of Multiple Faults in Rotating Machinery Using Ensemble Learning
Fault diagnosis of rotating machines is an important task to prevent machinery downtime, and provide verifiable support for condition-based maintenance (CBM) decision-making. Deep learning-enabled fault diagnosis operations have become increasingly popular because features are extracted and selected...
Main Authors: | Udeme Ibanga Inyang, Ivan Petrunin, Ian Jennions |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-01-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/23/2/1005 |
Similar Items
-
Health Condition Estimation of Bearings with Multiple Faults by a Composite Learning-Based Approach
by: Udeme Inyang, et al.
Published: (2021-06-01) -
Effect of Shaft and Bearing Flexibility on Dynamic Behavior of Helical Gears: Modeling and Experimental Comparisons
by: Kai FENG
Published: (2012-11-01) -
Fault diagnosis for mine hoist bearing based on EMD method
by: QIAO Shuyun
Published: (2016-04-01) -
ROTATING MACHINERY FAULT DIAGNOSIS BASED ON TWO-DIMENSIONAL CONVOLUTION NEURAL NETWORK
by: ZHANG LiZhi, et al.
Published: (2020-01-01) -
Dataset of single and double faults scenarios using vibration signals from a rotary machine
by: Larry Marshall, et al.
Published: (2023-08-01)