Mutual Information and Meta-Heuristic Classifiers Applied to Bearing Fault Diagnosis in Three-Phase Induction Motors
Three-phase induction motors are extensively used in industrial processes due to their robustness, adaptability to different operating conditions, and low operation and maintenance costs. Induction motor fault diagnosis has received special attention from industry since it can reduce process losses...
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MDPI AG
2021-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/1/314 |
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doaj-c9cf3599148a4e109b3eb5b0a0077f432020-12-31T00:05:44ZengMDPI AGApplied Sciences2076-34172021-12-011131431410.3390/app11010314Mutual Information and Meta-Heuristic Classifiers Applied to Bearing Fault Diagnosis in Three-Phase Induction MotorsGustavo Henrique Bazan0Alessandro Goedtel1Marcelo Favoretto Castoldi2Wagner Fontes Godoy3Oscar Duque-Perez4Daniel Morinigo-Sotelo5Department of Electromechanics, Federal Institute of Paraná, Av. Doutor Tito, s/n, Jacarezinho 86400-000, PR, BrazilDepartment of Electrical Engineering, Federal University of Technology-Paraná, Av. Alberto Carazzai, 1640, Cornélio Procópio 86300-000, PR, BrazilDepartment of Electrical Engineering, Federal University of Technology-Paraná, Av. Alberto Carazzai, 1640, Cornélio Procópio 86300-000, PR, BrazilDepartment of Electrical Engineering, Federal University of Technology-Paraná, Av. Alberto Carazzai, 1640, Cornélio Procópio 86300-000, PR, BrazilDepartment of Electrical Engineering, University of Valladolid, Paseo del Cauce, 59, 47011 Valladolid, SpainDepartment of Electrical Engineering, University of Valladolid, Paseo del Cauce, 59, 47011 Valladolid, SpainThree-phase induction motors are extensively used in industrial processes due to their robustness, adaptability to different operating conditions, and low operation and maintenance costs. Induction motor fault diagnosis has received special attention from industry since it can reduce process losses and ensure the reliable operation of industrial systems. Therefore, this paper presents a study on the use of meta-heuristic tools in the diagnosis of bearing failures in induction motors. The extraction of the fault characteristics is performed based on mutual information measurements between the stator current signals in the time domain. Then, the Artificial Bee Colony algorithm is used to select the relevant mutual information values and optimize the pattern classifier input data. To evaluate the classification accuracy under various levels of failure severity, the performance of two different pattern classifiers was compared: The C4.5 decision tree and the multi-layer artificial perceptron neural networks. The experimental results confirm the effectiveness of the proposed approach.https://www.mdpi.com/2076-3417/11/1/314bearing failure diagnosismutual informationartificial bee colonypattern recognition |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Gustavo Henrique Bazan Alessandro Goedtel Marcelo Favoretto Castoldi Wagner Fontes Godoy Oscar Duque-Perez Daniel Morinigo-Sotelo |
| spellingShingle |
Gustavo Henrique Bazan Alessandro Goedtel Marcelo Favoretto Castoldi Wagner Fontes Godoy Oscar Duque-Perez Daniel Morinigo-Sotelo Mutual Information and Meta-Heuristic Classifiers Applied to Bearing Fault Diagnosis in Three-Phase Induction Motors Applied Sciences bearing failure diagnosis mutual information artificial bee colony pattern recognition |
| author_facet |
Gustavo Henrique Bazan Alessandro Goedtel Marcelo Favoretto Castoldi Wagner Fontes Godoy Oscar Duque-Perez Daniel Morinigo-Sotelo |
| author_sort |
Gustavo Henrique Bazan |
| title |
Mutual Information and Meta-Heuristic Classifiers Applied to Bearing Fault Diagnosis in Three-Phase Induction Motors |
| title_short |
Mutual Information and Meta-Heuristic Classifiers Applied to Bearing Fault Diagnosis in Three-Phase Induction Motors |
| title_full |
Mutual Information and Meta-Heuristic Classifiers Applied to Bearing Fault Diagnosis in Three-Phase Induction Motors |
| title_fullStr |
Mutual Information and Meta-Heuristic Classifiers Applied to Bearing Fault Diagnosis in Three-Phase Induction Motors |
| title_full_unstemmed |
Mutual Information and Meta-Heuristic Classifiers Applied to Bearing Fault Diagnosis in Three-Phase Induction Motors |
| title_sort |
mutual information and meta-heuristic classifiers applied to bearing fault diagnosis in three-phase induction motors |
| publisher |
MDPI AG |
| series |
Applied Sciences |
| issn |
2076-3417 |
| publishDate |
2021-12-01 |
| description |
Three-phase induction motors are extensively used in industrial processes due to their robustness, adaptability to different operating conditions, and low operation and maintenance costs. Induction motor fault diagnosis has received special attention from industry since it can reduce process losses and ensure the reliable operation of industrial systems. Therefore, this paper presents a study on the use of meta-heuristic tools in the diagnosis of bearing failures in induction motors. The extraction of the fault characteristics is performed based on mutual information measurements between the stator current signals in the time domain. Then, the Artificial Bee Colony algorithm is used to select the relevant mutual information values and optimize the pattern classifier input data. To evaluate the classification accuracy under various levels of failure severity, the performance of two different pattern classifiers was compared: The C4.5 decision tree and the multi-layer artificial perceptron neural networks. The experimental results confirm the effectiveness of the proposed approach. |
| topic |
bearing failure diagnosis mutual information artificial bee colony pattern recognition |
| url |
https://www.mdpi.com/2076-3417/11/1/314 |
| work_keys_str_mv |
AT gustavohenriquebazan mutualinformationandmetaheuristicclassifiersappliedtobearingfaultdiagnosisinthreephaseinductionmotors AT alessandrogoedtel mutualinformationandmetaheuristicclassifiersappliedtobearingfaultdiagnosisinthreephaseinductionmotors AT marcelofavorettocastoldi mutualinformationandmetaheuristicclassifiersappliedtobearingfaultdiagnosisinthreephaseinductionmotors AT wagnerfontesgodoy mutualinformationandmetaheuristicclassifiersappliedtobearingfaultdiagnosisinthreephaseinductionmotors AT oscarduqueperez mutualinformationandmetaheuristicclassifiersappliedtobearingfaultdiagnosisinthreephaseinductionmotors AT danielmorinigosotelo mutualinformationandmetaheuristicclassifiersappliedtobearingfaultdiagnosisinthreephaseinductionmotors |
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