| Model-based fault detection and isolation of a steam generator using neuro-fuzzy networks R Razavi-Far, H Davilu, V Palade, C Lucas Neurocomputing 72 (13-15), 2939-2951, 2009 | 88 | 2009 |
| An integrated class-imbalanced learning scheme for diagnosing bearing defects in induction motors R Razavi-Far, M Farajzadeh-Zanjani, M Saif IEEE Transactions on Industrial Informatics 13 (6), 2758-2769, 2017 | 56 | 2017 |
| Bagged ensemble of Fuzzy C-Means classifiers for nuclear transient identification P Baraldi, R Razavi-Far, E Zio Annals of Nuclear Energy 38 (5), 1161-1171, 2011 | 42 | 2011 |
| Information fusion and semi-supervised deep learning scheme for diagnosing gear faults in induction machine systems R Razavi-Far, E Hallaji, M Farajzadeh-Zanjani, M Saif, SH Kia, H Henao, ... IEEE Transactions on Industrial Electronics 66 (8), 6331-6342, 2018 | 36 | 2018 |
| Classifier-ensemble incremental-learning procedure for nuclear transient identification at different operational conditions P Baraldi, R Razavi-Far, E Zio Reliability Engineering & System Safety 96 (4), 480-488, 2011 | 35 | 2011 |
| Dynamic weighting ensembles for incremental learning and diagnosing new concept class faults in nuclear power systems R Razavi-Far, P Baraldi, E Zio IEEE Transactions on Nuclear Science 59 (5), 2520-2530, 2012 | 32 | 2012 |
| zSlices-based general type-2 fuzzy fusion of support vector machines with application to bearing fault detection H Hassani, J Zarei, MM Arefi, R Razavi-Far IEEE Transactions on Industrial Electronics 64 (9), 7210-7217, 2017 | 31 | 2017 |
| A multiple observers and dynamic weighting ensembles scheme for diagnosing new class faults in wind turbines R Razavi-Far, M Kinnaert Control Engineering Practice 21 (9), 1165-1177, 2013 | 29 | 2013 |
| Efficient residuals pre-processing for diagnosing multi-class faults in a doubly fed induction generator, under missing data scenarios R Razavi-Far, E Zio, V Palade Expert Systems with Applications 41 (14), 6386-6399, 2014 | 25 | 2014 |
| Neuro-fuzzy based fault diagnosis of a steam generator R Razavi-Far, H Davilu, V Palade, C Lucas IFAC Proceedings Volumes 42 (8), 1180-1185, 2009 | 22 | 2009 |
| A semi-supervised diagnostic framework based on the surface estimation of faulty distributions R Razavi-Far, E Hallaji, M Farajzadeh-Zanjani, M Saif IEEE Transactions on Industrial Informatics 15 (3), 1277-1286, 2018 | 21 | 2018 |
| Failure prognosis and applications—A survey of recent literature M Kordestani, M Saif, ME Orchard, R Razavi-Far, K Khorasani IEEE transactions on reliability, 2019 | 20 | 2019 |
| Efficient feature extraction of vibration signals for diagnosing bearing defects in induction motors M Farajzadeh-Zanjani, R Razavi-Far, M Saif, L Rueda 2016 International Joint Conference on Neural Networks (IJCNN), 4504-4511, 2016 | 20 | 2016 |
| Diagnosis of bearing defects in induction motors by fuzzy-neighborhood density-based clustering M Farajzadeh-Zanjani, R Razavi-Far, M Saif, J Zarei, V Palade 2015 IEEE 14th International Conference on Machine Learning and Applications …, 2015 | 20 | 2015 |
| A method for estimating the confidence in the identification of nuclear transients by a bagged ensemble of FCM classifiers P Baraldi, R Razavi-Far, E Zio | 17 | 2010 |
| Efficient sampling techniques for ensemble learning and diagnosing bearing defects under class imbalanced condition M Farajzadeh-Zanjani, R Razavi-Far, M Saif 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 1-7, 2016 | 16 | 2016 |
| Data-driven prognostic techniques for estimation of the remaining useful life of lithium-ion batteries R Razavi-Far, M Farajzadeh-Zanjani, S Chakrabarti, M Saif 2016 IEEE International Conference on Prognostics and Health Management …, 2016 | 16 | 2016 |
| Incremental design of a decision system for residual evaluation: A wind turbine application R Razavi-Far, M Kinnaert IFAC Proceedings Volumes 45 (20), 343-348, 2012 | 16 | 2012 |
| Optimal detection of new classes of faults by an invasive weed optimization method R Razavi-Far, V Palade, E Zio 2014 International Joint Conference on Neural Networks (IJCNN), 91-98, 2014 | 15 | 2014 |
| A novelty detector and extreme verification latency model for nonstationary environments R Razavi-Far, E Hallaji, M Saif, G Ditzler IEEE Transactions on Industrial Electronics 66 (1), 561-570, 2019 | 14 | 2019 |
Mehrdad SaifUniversity of WindsorVerified email at uwindsor.ca
