Offline and online fault detection and diagnosis of induction motors using a hybrid soft computing model
- 1 December 2013
- journal article
- Published by Elsevier BV in Applied Soft Computing
- Vol. 13 (12), 4493-4507
- https://doi.org/10.1016/j.asoc.2013.08.002
Abstract
No abstract availableThis publication has 44 references indexed in Scilit:
- Rotor fault condition monitoring techniques for squirrel-cage induction machine—A reviewMechanical Systems and Signal Processing, 2011
- A new method for early fault detection and diagnosis of broken rotor barsEnergy Conversion and Management, 2011
- Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiersEnergy, 2010
- Mixed-fault diagnosis in induction motors considering varying load and broken bars locationEnergy Conversion and Management, 2010
- Automatic detection and classification of rotor cage faults in squirrel cage induction motorNeural Computing & Applications, 2009
- FDI based on pattern recognition using Kalman prediction: Application to an induction machineEngineering Applications of Artificial Intelligence, 2008
- Landmine Detection and Classification With Complex-Valued Hybrid Neural Network Using Scattering Parameters DatasetIEEE Transactions on Neural Networks, 2005
- Novel frequency-domain-based technique to detect stator interturn faults in induction machines using stator-induced voltages after switch-offIEEE Transactions on Industry Applications, 2002
- Rule Extraction: From Neural Architecture to Symbolic RepresentationConnection Science, 1995
- Bootstrap Methods: Another Look at the JackknifeThe Annals of Statistics, 1979