Improved Classification by Non Iterative and Ensemble Classifiers in Motor Fault Diagnosis
Data driven approach for multi-class fault diagnosis of induction motor using MCSA at steady state condition is a complex pattern classification problem. This investigation has exploited the built-in ensemble process of non-iterative classifiers to resolve the most challenging issues in this area,...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Stefan cel Mare University of Suceava
2018-02-01
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| Series: | Advances in Electrical and Computer Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.4316/AECE.2018.01012 |
| _version_ | 1811324618006331392 |
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| author | PANIGRAHY, P. S. CHATTOPADHYAY, P. |
| author_facet | PANIGRAHY, P. S. CHATTOPADHYAY, P. |
| author_sort | PANIGRAHY, P. S. |
| collection | DOAJ |
| description | Data driven approach for multi-class fault diagnosis of induction motor using MCSA at steady state condition
is a complex pattern classification problem. This investigation has exploited the built-in ensemble process
of non-iterative classifiers to resolve the most challenging issues in this area, including bearing and
stator fault detection. Non-iterative techniques exhibit with an average 15% of increased fault classification
accuracy against their iterative counterparts. Particularly RF has shown outstanding performance even
at less number of training samples and noisy feature space because of its distributive feature model.
The robustness of the results, backed by the experimental verification shows that the non-iterative
individual classifiers like RF is the optimum choice in the area of automatic fault diagnosis of
induction motor. |
| first_indexed | 2024-04-13T14:17:24Z |
| format | Article |
| id | doaj.art-47aa0b5fb89d4e13aff3ad478bdd8c18 |
| institution | Directory Open Access Journal |
| issn | 1582-7445 1844-7600 |
| language | English |
| last_indexed | 2024-04-13T14:17:24Z |
| publishDate | 2018-02-01 |
| publisher | Stefan cel Mare University of Suceava |
| record_format | Article |
| series | Advances in Electrical and Computer Engineering |
| spelling | doaj.art-47aa0b5fb89d4e13aff3ad478bdd8c182022-12-22T02:43:36ZengStefan cel Mare University of SuceavaAdvances in Electrical and Computer Engineering1582-74451844-76002018-02-011819510410.4316/AECE.2018.01012Improved Classification by Non Iterative and Ensemble Classifiers in Motor Fault DiagnosisPANIGRAHY, P. S.CHATTOPADHYAY, P.Data driven approach for multi-class fault diagnosis of induction motor using MCSA at steady state condition is a complex pattern classification problem. This investigation has exploited the built-in ensemble process of non-iterative classifiers to resolve the most challenging issues in this area, including bearing and stator fault detection. Non-iterative techniques exhibit with an average 15% of increased fault classification accuracy against their iterative counterparts. Particularly RF has shown outstanding performance even at less number of training samples and noisy feature space because of its distributive feature model. The robustness of the results, backed by the experimental verification shows that the non-iterative individual classifiers like RF is the optimum choice in the area of automatic fault diagnosis of induction motor.http://dx.doi.org/10.4316/AECE.2018.01012discrete wavelet transformsfault diagnosisfeature extractioninduction motorsmachine learning |
| spellingShingle | PANIGRAHY, P. S. CHATTOPADHYAY, P. Improved Classification by Non Iterative and Ensemble Classifiers in Motor Fault Diagnosis Advances in Electrical and Computer Engineering discrete wavelet transforms fault diagnosis feature extraction induction motors machine learning |
| title | Improved Classification by Non Iterative and Ensemble Classifiers in Motor Fault Diagnosis |
| title_full | Improved Classification by Non Iterative and Ensemble Classifiers in Motor Fault Diagnosis |
| title_fullStr | Improved Classification by Non Iterative and Ensemble Classifiers in Motor Fault Diagnosis |
| title_full_unstemmed | Improved Classification by Non Iterative and Ensemble Classifiers in Motor Fault Diagnosis |
| title_short | Improved Classification by Non Iterative and Ensemble Classifiers in Motor Fault Diagnosis |
| title_sort | improved classification by non iterative and ensemble classifiers in motor fault diagnosis |
| topic | discrete wavelet transforms fault diagnosis feature extraction induction motors machine learning |
| url | http://dx.doi.org/10.4316/AECE.2018.01012 |
| work_keys_str_mv | AT panigrahyps improvedclassificationbynoniterativeandensembleclassifiersinmotorfaultdiagnosis AT chattopadhyayp improvedclassificationbynoniterativeandensembleclassifiersinmotorfaultdiagnosis |