ANN- and ANFIS-based multi-staged decision algorithm for the detection and diagnosis of bearing faults
- 3 April 2012
- journal article
- research article
- Published by Springer Science and Business Media LLC in Neural Computing & Applications
- Vol. 22 (S1), 435-446
- https://doi.org/10.1007/s00521-012-0912-7
Abstract
No abstract availableKeywords
This publication has 19 references indexed in Scilit:
- Rolling element bearing diagnostics—A tutorialMechanical Systems and Signal Processing, 2011
- Fault Current Limiting Characteristics of Separated and Integrated Three-Phase Flux-Lock Type SFCLsJournal of Electrical Engineering & Technology, 2007
- Estimation of the running speed and bearing defect frequencies of an induction motor from vibration dataMechanical Systems and Signal Processing, 2004
- Basic vibration signal processing for bearing fault detectionIEEE Transactions on Education, 2003
- Neural-network-based motor rolling bearing fault diagnosisIEEE Transactions on Industrial Electronics, 2000
- A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearingsTribology International, 1999
- Using a neural/fuzzy system to extract heuristic knowledge of incipient faults in induction motors. Part I-MethodologyIEEE Transactions on Industrial Electronics, 1995
- ANFIS: adaptive-network-based fuzzy inference systemIEEE Transactions on Systems, Man, and Cybernetics, 1993
- The vibration produced by multiple point defects in a rolling element bearingJournal of Sound and Vibration, 1985
- Model for the vibration produced by a single point defect in a rolling element bearingJournal of Sound and Vibration, 1984