Study of Rotor Asymmetry Effects of an Induction Machine by Finite Element Method

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“…On the other hand, frequencies can appear in the stator current spectrum can be writing as follows [25][26]:…”

Section: Motor Test Benchmentioning

confidence: 99%

A comparative study dedicated to rotor failure detection in induction motors using MCSA, DWT, and EMD techniques

Sbaa

Bessous

Pusca

et al. 2020

2020 International Conference on Electrical Engineering (ICEE)

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“…Nowadays, maintenance cost reductions are the number one priority for electrical drive to prevent unscheduled downtimes and to increase operational effectiveness. Recent advances of signal processing techniques, such as artificial neural networks [3][4][5][6][7][8], wavelets [9], etc.., have provided more powerful tools for fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Classification of Induction Machine Faults using Time Frequency Representation and Particle Swarm Optimization

Medoued¹,

Lebaroud²,

Laïfa³

et al. 2014

Journal of Electrical Engineering and Technology

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-This paper presents a new method of classification of the induction machine faults using Time Frequency Representation, Particle Swarm Optimization and artificial neural network. The essence of the feature extraction is to project from faulty machine to a low size signal time-frequency representation (TFR), which is deliberately designed for maximizing the separability between classes, a distinct TFR is designed for each class. The feature vectors size is optimized using Particle Swarm Optimization method (PSO). The classifier is designed using an artificial neural network. This method allows an accurate classification independently of load level. The introduction of the PSO in the classification procedure has given good results using the reduced size of the feature vectors obtained by the optimization process. These results are validated on a 5.5-kW induction motor test bench.

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A detailed study of the spectral content in the stator current of asynchronous machines under broken rotor bar faults using MCSA technique

Bessous

Sbaa

Toumi

2018

2018 6th International Conference on Control Engineering &Amp; Information Technology (CEIT)

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Study of Rotor Asymmetry Effects of an Induction Machine by Finite Element Method

Cited by 10 publications

References 13 publications

A comparative study dedicated to rotor failure detection in induction motors using MCSA, DWT, and EMD techniques

A comparative study dedicated to rotor failure detection in induction motors using MCSA, DWT, and EMD techniques

Classification of Induction Machine Faults using Time Frequency Representation and Particle Swarm Optimization

A detailed study of the spectral content in the stator current of asynchronous machines under broken rotor bar faults using MCSA technique

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