6533b861fe1ef96bd12c4505

RESEARCH PRODUCT

Fault diagnosis of induction motors broken rotor bars by pattern recognition based on noise cancelation

Hamid Reza KarimiJafar ZareiMohammad Amin Tajeddini

subject

removing irrelevant fault componentsEngineeringArtificial neural networkneural networkRotor (electric)Bar (music)business.industryComputer Science::Neural and Evolutionary ComputationFilter (signal processing)Fault (power engineering)law.inventionNoisefault diagnosis and classificationControl and Systems Engineeringlawfault diagnosis and classification; neural network; removing irrelevant fault components; Stator current signal monitoring; Electrical and Electronic Engineering; Control and Systems EngineeringElectronic engineeringTime domainElectrical and Electronic EngineeringStator current signal monitoringbusinessAlgorithmInduction motor

description

Current signal monitoring (CSM) can be used as an effective tool for diagnosing broken rotor bars fault in induction motors. In this paper, fault diagnosis and classification based on artificial neural networks (ANNs) is done in two stages. In the first stage, a filter is designed to remove irrelevant fault components (such as noise) of current signals. The coefficients of the filter are obtained by least square (LS) algorithm. Then by extracting suitable time domain features from filter's output, a neural network is trained for fault classification. The output vector of this network is represented in one of four categories that includes healthy mode, a 5 mm crack on a bar, one broken bar, and two broken bar modes. An optimum structure of the neural network is obtained via particle swarm optimization (PSO) algorithm.

yearjournalcountryeditionlanguage
2014-06-012014 IEEE 23rd International Symposium on Industrial Electronics (ISIE)
https://doi.org/10.1109/isie.2014.6865004