Improved Diagnostic Approach for BRB Detection and Classification in Inverter-Driven Induction Motors Employing Sparse Stacked Autoencoder (SSAE) and LightGBM
This study introduces an innovative approach to diagnostics, employing a unique combination of techniques including a stratified group K-fold cross-validation method and a sparse stacked autoencoder (SSAE) alongside LightGBM. By examining signatures derived from motor current, voltage, speed, and to...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/13/7/1292 |
| _version_ | 1797212719244378112 |
|---|---|
| author | Muhammad Amir Khan Bilal Asad Toomas Vaimann Ants Kallaste |
| author_facet | Muhammad Amir Khan Bilal Asad Toomas Vaimann Ants Kallaste |
| author_sort | Muhammad Amir Khan |
| collection | DOAJ |
| description | This study introduces an innovative approach to diagnostics, employing a unique combination of techniques including a stratified group K-fold cross-validation method and a sparse stacked autoencoder (SSAE) alongside LightGBM. By examining signatures derived from motor current, voltage, speed, and torque, the framework aims to effectively detect and classify broken rotor bars (BRBs) within inverter-fed induction machines. In this kind of cross-validation method, class labels and grouping factors are spread out across folds by distributing motor operational data attributes equally over target label stratification and extra grouping information. By integrating SSAE and LightGBM, a gradient-boosting framework, we elevate the precision and efficacy of defect diagnosis. The SSAE feature extraction algorithm proves to be particularly effective in identifying small BRB signatures within motor operational data. Our approach relies on comprehensive datasets collected from motor systems operating under diverse loading conditions, ranging from 0% to 100%. Using a sparse stacked autoencoder, the model lowers the dimensionality and noise of the motor fault data. It then sends the cleaned data to the LightGBM network for fault diagnosis. LightGBM leverages the attributes of the sparse stacked autoencoder to showcase the distinctive qualities associated with BRBs. This integration offers the potential to improve defect identification by furnishing input representations that are both more precise and more concise. The proposed model (SSAE with LightGBM) was trained using 80% of the data, while the remaining 20% was used for testing. To validate the proposed architecture, we evaluate the accuracy, precision, recall, and F1-scores of the results using motor global signals, with the help of confusion matrices with receiver operating characteristic (ROC) curves. Following the training of a new LightGBM model with refined hyperparameters through Bayesian optimization, we proceed to conduct the final classification utilizing the optimal feature subset. Evaluation of the test dataset indicates that the BRBs diagnostic framework facilitates the detection and classification of issues with induction motor BRBs, achieving accuracy rates of up to 99% across all loading conditions. |
| first_indexed | 2024-04-24T10:46:51Z |
| format | Article |
| id | doaj.art-9963f0d0ef8242a4b961bb66c3c29cb2 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-04-24T10:46:51Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-9963f0d0ef8242a4b961bb66c3c29cb22024-04-12T13:17:18ZengMDPI AGElectronics2079-92922024-03-01137129210.3390/electronics13071292Improved Diagnostic Approach for BRB Detection and Classification in Inverter-Driven Induction Motors Employing Sparse Stacked Autoencoder (SSAE) and LightGBMMuhammad Amir Khan0Bilal Asad1Toomas Vaimann2Ants Kallaste3Department of Electrical (Power) Engineering, The Islamia University of Bahawalpur, Bahawalpur 6300, PakistanDepartment of Electrical (Power) Engineering, The Islamia University of Bahawalpur, Bahawalpur 6300, PakistanDepartment of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 12616 Tallinn, EstoniaDepartment of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 12616 Tallinn, EstoniaThis study introduces an innovative approach to diagnostics, employing a unique combination of techniques including a stratified group K-fold cross-validation method and a sparse stacked autoencoder (SSAE) alongside LightGBM. By examining signatures derived from motor current, voltage, speed, and torque, the framework aims to effectively detect and classify broken rotor bars (BRBs) within inverter-fed induction machines. In this kind of cross-validation method, class labels and grouping factors are spread out across folds by distributing motor operational data attributes equally over target label stratification and extra grouping information. By integrating SSAE and LightGBM, a gradient-boosting framework, we elevate the precision and efficacy of defect diagnosis. The SSAE feature extraction algorithm proves to be particularly effective in identifying small BRB signatures within motor operational data. Our approach relies on comprehensive datasets collected from motor systems operating under diverse loading conditions, ranging from 0% to 100%. Using a sparse stacked autoencoder, the model lowers the dimensionality and noise of the motor fault data. It then sends the cleaned data to the LightGBM network for fault diagnosis. LightGBM leverages the attributes of the sparse stacked autoencoder to showcase the distinctive qualities associated with BRBs. This integration offers the potential to improve defect identification by furnishing input representations that are both more precise and more concise. The proposed model (SSAE with LightGBM) was trained using 80% of the data, while the remaining 20% was used for testing. To validate the proposed architecture, we evaluate the accuracy, precision, recall, and F1-scores of the results using motor global signals, with the help of confusion matrices with receiver operating characteristic (ROC) curves. Following the training of a new LightGBM model with refined hyperparameters through Bayesian optimization, we proceed to conduct the final classification utilizing the optimal feature subset. Evaluation of the test dataset indicates that the BRBs diagnostic framework facilitates the detection and classification of issues with induction motor BRBs, achieving accuracy rates of up to 99% across all loading conditions.https://www.mdpi.com/2079-9292/13/7/1292electrical machinescondition monitoringinduction motorsrotor faultsfeature importancebroken rotor bars diagnosis |
| spellingShingle | Muhammad Amir Khan Bilal Asad Toomas Vaimann Ants Kallaste Improved Diagnostic Approach for BRB Detection and Classification in Inverter-Driven Induction Motors Employing Sparse Stacked Autoencoder (SSAE) and LightGBM Electronics electrical machines condition monitoring induction motors rotor faults feature importance broken rotor bars diagnosis |
| title | Improved Diagnostic Approach for BRB Detection and Classification in Inverter-Driven Induction Motors Employing Sparse Stacked Autoencoder (SSAE) and LightGBM |
| title_full | Improved Diagnostic Approach for BRB Detection and Classification in Inverter-Driven Induction Motors Employing Sparse Stacked Autoencoder (SSAE) and LightGBM |
| title_fullStr | Improved Diagnostic Approach for BRB Detection and Classification in Inverter-Driven Induction Motors Employing Sparse Stacked Autoencoder (SSAE) and LightGBM |
| title_full_unstemmed | Improved Diagnostic Approach for BRB Detection and Classification in Inverter-Driven Induction Motors Employing Sparse Stacked Autoencoder (SSAE) and LightGBM |
| title_short | Improved Diagnostic Approach for BRB Detection and Classification in Inverter-Driven Induction Motors Employing Sparse Stacked Autoencoder (SSAE) and LightGBM |
| title_sort | improved diagnostic approach for brb detection and classification in inverter driven induction motors employing sparse stacked autoencoder ssae and lightgbm |
| topic | electrical machines condition monitoring induction motors rotor faults feature importance broken rotor bars diagnosis |
| url | https://www.mdpi.com/2079-9292/13/7/1292 |
| work_keys_str_mv | AT muhammadamirkhan improveddiagnosticapproachforbrbdetectionandclassificationininverterdriveninductionmotorsemployingsparsestackedautoencoderssaeandlightgbm AT bilalasad improveddiagnosticapproachforbrbdetectionandclassificationininverterdriveninductionmotorsemployingsparsestackedautoencoderssaeandlightgbm AT toomasvaimann improveddiagnosticapproachforbrbdetectionandclassificationininverterdriveninductionmotorsemployingsparsestackedautoencoderssaeandlightgbm AT antskallaste improveddiagnosticapproachforbrbdetectionandclassificationininverterdriveninductionmotorsemployingsparsestackedautoencoderssaeandlightgbm |