Skip to main navigation
Skip to search
Skip to main content
University of Johannesburg Home
Home
Scholars
Research entities
Research output
Press/Media
Equipment & facilities
Prestigious awards
Search by expertise, name or affiliation
Probabilistic fault identification using vibration data and neural networks
T. Marwala
, H. E.M. Hunt
University of Cambridge
Research output
:
Contribution to journal
›
Article
›
peer-review
1
Citation (Scopus)
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Probabilistic fault identification using vibration data and neural networks'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Addition Mode
14%
Bayesian Formulation
14%
Confidence Interval
14%
Coordinate Modal Assurance Criterion (COMAC)
57%
Cylindrical Shell
28%
Data Networks
100%
Energy Forms
14%
Fault Identification
100%
Frequency Response Function
28%
Frequency Vector
14%
Hybrid Monte Carlo
14%
Imaginary Component
14%
Modal Energy
100%
Modal Properties
57%
Natural Frequency
28%
Network Coordinate
14%
Neural Network
100%
Probabilistic Faults
100%
Real Component
14%
Substructure
28%
Two-frequency
14%
Vibration Data
100%
Engineering
Confidence Interval
14%
Cylindrical Shell
28%
Frequency Response Function
28%
Main Advantage
14%
Modal Energy
100%
Natural Frequency
28%
Earth and Planetary Sciences
Confidence Interval
50%
Cylindrical Shell
100%
Standard Deviation
50%