scholarly journals Optimization of Induction Motors Using Design of Experiments and Particle Swarm Optimization

10.5772/38665 ◽  
2012 ◽  
Author(s):  
Houssem Rafik El-Hana Bouchekara ◽  
Mohammed Simsim ◽  
Makbul Anwari
Keyword(s):  
Particle Swarm Optimization ◽  
Design Of Experiments ◽  
Induction Motors ◽  
Particle Swarm ◽  
Swarm Optimization

scholarly journals Condition monitoring of Induction motors through Simulation of Bearing Fault and Air Gap Eccentricity Fault

2019 ◽  
Vol 8 (3) ◽  
pp. 176-193
Keyword(s):  
Particle Swarm Optimization ◽  
Induction Motors ◽  
Fitness Function ◽  
Particle Swarm ◽  
Air Gap ◽  
Percentage Error ◽  
Swarm Optimization ◽  
Bearing Fault ◽  
Modified Particle Swarm Optimization ◽  
Comparison Results

Induction motors have an important role in the industry on account of their advantages over other electrical motors. Consequently, there is a huge demand for their safe and sound operation. But it is not free from failures, which result in unnecessary downtimes and create great losses with regards to both revenue and maintenance. For that reason, early fault detection is considered necessary for the safety maintenance of the motor. In the present circumstances, the health monitoring of the induction motors are progressively increasing due to its potential to enhance operating costs, increase the reliability of function and so does the current paper emerge. Also, this paper deals with a novel effective technique for detecting the bearing fault and air gap eccentricity fault of the induction motor. Summarization and analysis of the findings are done based on percentage error and fitness function Value. Comparison results of bad bearing faults and air gap eccentricity are given separately in the paper. The findings of the study concluded that particle swarm optimization (PSO) can be considered as better optimization for bad bearing fault whereas modified particle swarm optimization is concluded as better optimization for air gap eccentricity fault.

Model-based fault diagnosis of induction motor eccentricity using particle swarm optimization

2008 ◽  
Vol 223 (3) ◽  
pp. 607-615 ◽  
Author(s):  
A Nikranjbar ◽  
M Ebrahimi ◽  
A S Wood
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Detection ◽  
Induction Motors ◽  
Particle Swarm ◽  
Detection Algorithm ◽  
Fault Detection And Diagnosis ◽  
Adaptive Synchronization ◽  
Swarm Optimization ◽  
Model Based ◽  
Model Free

Much research works address model-free or signal processing and spectral analysis-based fault detection schemes for rotor eccentricity fault in induction motors. Nevertheless, despite existing reliable fault-embedded eccentricity mathematical models such as the winding function method an integrated model-based fault detection algorithm for detecting this fault yet has not been fully explored. This article presents model-based mixed-eccentricity fault detection and diagnosis for induction motors. The proposed algorithm can successfully detect faults and their severity using stator currents. To determine the values of the fault-related parameters, an adaptive synchronization-based parameter estimation algorithm is introduced using particle swarm optimization. Simulation and experiments demonstrate the ability of the algorithm to detect and diagnose these faults. The proposed algorithm can be employed to estimate the parameters, in addition to slowly time varying and abruptly changing parameters.

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