Three-Phase Induction Motors Faults Recognition and Classification Using Neural Networks and Response Surface Models

Júnior, Arismar Morais Gonçalves; Silva, Valceres Vieira Rocha e; Baccarini, Lane Maria Rabelo; Reis, Maria Luíza Figueiredo

doi:10.1007/s40313-014-0113-y

Cited by 11 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Neural networks models with a decision structure are presented in Ref. [92] to analyse the bearing localized defects. The results show good performance of the implemented model and its ability to identify the bearing localized faults.…”

Section: Mcsa For Bearing Localized Defectsmentioning

confidence: 99%

Fault Diagnosis and Detection

Mahmoud

Xia

2013

Analysis and Synthesis of Fault‐Tolerant Control Systems

View full text Add to dashboard Cite

Here he has been involved in research on structural health monitoring and energy harvesting. He currently works at the Department of Mechatronics Engineering, Marmara University, as an Assoc. Prof. Dr. His research interests are fault diagnosis, energy harvesting, intelligent materials, automation systems, structural health monitoring, and artificial intelligence systems. He is the director of Fault Diagnosis and Structural Health Monitoring Lab in Marmara University. He is an editorial board member at many international journals. He has publications in many international journals and symposiums on failure diagnosis and fault detection.Dr. Muhammet Ünal works as an assistant professor in the Department of Mechatronics Engineering, Marmara University, Istanbul (Turkey). His research interests are system identification, parameter optimization, real-time control, and structural health monitoring. His primary research interests are vibration analysis and fault diagnosis of mechanical systems. Dr. Ünal has written several papers in vrious fields of mechatronics engineering and one book on control engineering. He is also the first author of the book Optimization of PID Controllers Using Ant Colony and Genetic Algorithms, which is published in Springer.

show abstract

Section: Mcsa For Bearing Localized Defectsmentioning

confidence: 99%