Convolutional neural network with batch normalisation for fault detection in squirrel cage induction motor
Abstract Early fault detection in an induction motor is the need of modern industries for minimal downtime and maximum production. A learning technique known as the Convolutional Neural network (CNN) provides automated and reliable feature extraction and selection. Considering these inherent traits...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | IET Electric Power Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/elp2.12005 |
| _version_ | 1797989537921630208 |
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| author | Prashant Kumar Ananda Shankar Hati |
| author_facet | Prashant Kumar Ananda Shankar Hati |
| author_sort | Prashant Kumar |
| collection | DOAJ |
| description | Abstract Early fault detection in an induction motor is the need of modern industries for minimal downtime and maximum production. A learning technique known as the Convolutional Neural network (CNN) provides automated and reliable feature extraction and selection. Considering these inherent traits of CNN, this study proposes a CNN in combination with batch normalisation (BN)‐based fault detection approach for simultaneous detection of bearing fault and broken rotor bars in squirrel cage induction motors (SCIMs). The SCIM vibration signals have different patterns for different defects, and the architecture of CNN is used in this study for fault diagnosis. For an efficient fault feature extraction, the proposed method uses CNN having multiple stacked layers with BN for faster training. In the proposed method, a CNN model with small kernel size is used along with adaptive gradient optimizer and BN to avoid performance degradation and optimum results. For the validation of the proposed technique, a test set‐up is used along with different fault conditions. The proposed method is also compared with the existing state‐of‐the‐art methods to illustrate its effectiveness. |
| first_indexed | 2024-04-11T08:21:57Z |
| format | Article |
| id | doaj.art-f8ca0bbd767744d982312be96cde6513 |
| institution | Directory Open Access Journal |
| issn | 1751-8660 1751-8679 |
| language | English |
| last_indexed | 2024-04-11T08:21:57Z |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Electric Power Applications |
| spelling | doaj.art-f8ca0bbd767744d982312be96cde65132022-12-22T04:34:57ZengWileyIET Electric Power Applications1751-86601751-86792021-01-01151395010.1049/elp2.12005Convolutional neural network with batch normalisation for fault detection in squirrel cage induction motorPrashant Kumar0Ananda Shankar Hati1Department of Mining Machinery Engineering Indian Institute of Technology (Indian School of Mines) Dhanbad IndiaDepartment of Mining Machinery Engineering Indian Institute of Technology (Indian School of Mines) Dhanbad IndiaAbstract Early fault detection in an induction motor is the need of modern industries for minimal downtime and maximum production. A learning technique known as the Convolutional Neural network (CNN) provides automated and reliable feature extraction and selection. Considering these inherent traits of CNN, this study proposes a CNN in combination with batch normalisation (BN)‐based fault detection approach for simultaneous detection of bearing fault and broken rotor bars in squirrel cage induction motors (SCIMs). The SCIM vibration signals have different patterns for different defects, and the architecture of CNN is used in this study for fault diagnosis. For an efficient fault feature extraction, the proposed method uses CNN having multiple stacked layers with BN for faster training. In the proposed method, a CNN model with small kernel size is used along with adaptive gradient optimizer and BN to avoid performance degradation and optimum results. For the validation of the proposed technique, a test set‐up is used along with different fault conditions. The proposed method is also compared with the existing state‐of‐the‐art methods to illustrate its effectiveness.https://doi.org/10.1049/elp2.12005data handlingfault diagnosisfeature extractiongradient methodslearning (artificial intelligence)machine bearings |
| spellingShingle | Prashant Kumar Ananda Shankar Hati Convolutional neural network with batch normalisation for fault detection in squirrel cage induction motor IET Electric Power Applications data handling fault diagnosis feature extraction gradient methods learning (artificial intelligence) machine bearings |
| title | Convolutional neural network with batch normalisation for fault detection in squirrel cage induction motor |
| title_full | Convolutional neural network with batch normalisation for fault detection in squirrel cage induction motor |
| title_fullStr | Convolutional neural network with batch normalisation for fault detection in squirrel cage induction motor |
| title_full_unstemmed | Convolutional neural network with batch normalisation for fault detection in squirrel cage induction motor |
| title_short | Convolutional neural network with batch normalisation for fault detection in squirrel cage induction motor |
| title_sort | convolutional neural network with batch normalisation for fault detection in squirrel cage induction motor |
| topic | data handling fault diagnosis feature extraction gradient methods learning (artificial intelligence) machine bearings |
| url | https://doi.org/10.1049/elp2.12005 |
| work_keys_str_mv | AT prashantkumar convolutionalneuralnetworkwithbatchnormalisationforfaultdetectioninsquirrelcageinductionmotor AT anandashankarhati convolutionalneuralnetworkwithbatchnormalisationforfaultdetectioninsquirrelcageinductionmotor |