Induction motor fault detection and diagnosis using a current state space pattern recognition
- Resource Type
- Authors
- Vitor Pires; João Martins; Tito G. Amaral
- Source
- Pattern Recognition Letters. 32:321-328
- Subject
- Stator
Computer science
business.industry
Feature extraction
Pattern recognition
Fault (power engineering)
Fault detection and isolation
law.invention
Identification (information)
Artificial Intelligence
law
Signal Processing
Pattern recognition (psychology)
State space
Computer Vision and Pattern Recognition
Artificial intelligence
Invariant (mathematics)
Electric current
business
Software
Induction motor
- Language
- ISSN
- 0167-8655
In the last few decades the continuous monitoring of complex dynamic systems has become an increasingly important issue across diverse engineering areas. This paper presents a pattern recognition based system that uses visual-based efficient invariants features for continuous monitoring of induction motors. The procedures presented here are based on the image identification of the 3-D current state space patterns that allow the identification of distinct fault types and, furthermore, their corresponding severity. This automatic fault detection system deals with time-variant electric currents and is based on the identification of three-phase stator currents specified patterns. Several simulation and experimental results are also presented in order to verify the effectiveness of the proposed methodology.