Intelligent systems for faults monitoring and diagnosis in three-phase induction motors

Author(s)

Marcelo Suetake

• Document type: Doctoral Thesis
• Press: São Carlos.
• Institution: Universidade de São Paulo (USP). Escola de Engenharia de São Carlos (EESC/SBD)
• Defense date: 2012-04-11

Examining board members

Ivan Nunes da Silva; Manoel Luís de Aguiar; Walmir Matos Caminhas; Renato Crivellari Creppe; Diógenes Pereira Gonzaga

• Advisor: Ivan Nunes da Silva

Abstract

The objective of this thesis consists of the implementation of intelligent systems for three-phase induction motors fault diagnosis and condition monitoring. Therefore, an experimental test stand for stator winding inter-turn short circuit faults, broken rotor bar in squirrel cage and, finally, defective wheel bearing has been designed. The main focus is to propose a neural network approach, which uses spectral frequency analysis and principal component analysis techniques to detect broken rotor bar in squirrel cage induction motor. Induction motor operating at different load torque conditions and supplied with sinusoidal voltage supply and three-phase inverter at different frequency was considered in the experiment for methodology evaluation. (AU)

• FAPESP's process: 08/00004-8 - Design of Intelligent Systems Using DSP for Fault Identification in Three-Phase Induction Motors.
• Grantee: Marcelo Suetake
• Support Opportunities: Scholarships in Brazil - Doctorate