Fault Detection in Induction Machines Using Learning Models and Fourier Spectrum Image Analysis
Induction motors are essential components in industry due to their efficiency and cost-effectiveness. This study presents an innovative methodology for automatic fault detection by analyzing images generated from the Fourier spectra of current signals using deep learning techniques. A new preprocess...
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MDPI AG
2025-01-01
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| author | Kevin Barrera-Llanga Jordi Burriel-Valencia Angel Sapena-Bano Javier Martinez-Roman |
| author_facet | Kevin Barrera-Llanga Jordi Burriel-Valencia Angel Sapena-Bano Javier Martinez-Roman |
| author_sort | Kevin Barrera-Llanga |
| collection | DOAJ |
| description | Induction motors are essential components in industry due to their efficiency and cost-effectiveness. This study presents an innovative methodology for automatic fault detection by analyzing images generated from the Fourier spectra of current signals using deep learning techniques. A new preprocessing technique incorporating a distinctive background to enhance spectral feature learning is proposed, enabling the detection of four types of faults: healthy motor coupled to a generator with a broken bar (HGB), broken rotor bar (BRB), race bearing fault (RBF), and bearing ball fault (BBF). The dataset was generated from three-phase signals of an induction motor controlled by a Direct Torque Controller under various operating conditions (20–1500 rpm with 0–100% load), resulting in 4251 images. The model, based on a Visual Geometry Group (VGG) architecture with 19 layers, achieved an overall accuracy of 98%, with specific accuracies of 99% for RAF, 100% for BRB, 100% for RBF, and 95% for BBF. A new model interpretability was assessed using explainability techniques, which allowed for the identification of specific learning patterns. This analysis introduces a new approach by demonstrating how different convolutional blocks capture particular features: the first convolutional block captures signal shape, while the second identifies background features. Additionally, distinct convolutional layers were associated with each fault type: layer 9 for RAF, layer 13 for BRB, layer 16 for RBF, and layer 14 for BBF. This methodology offers a scalable solution for predictive maintenance in induction motors, effectively combining signal processing, computer vision, and explainability techniques. |
| format | Article |
| id | doaj-art-f0943a51ef3f447392b5f7a90170f9b0 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-f0943a51ef3f447392b5f7a90170f9b02025-01-24T13:49:03ZengMDPI AGSensors1424-82202025-01-0125247110.3390/s25020471Fault Detection in Induction Machines Using Learning Models and Fourier Spectrum Image AnalysisKevin Barrera-Llanga0Jordi Burriel-Valencia1Angel Sapena-Bano2Javier Martinez-Roman3Institute for Energy Engineering, Universitat Politècnica de València, Camino. de Vera s/n, 46022 Valencia, SpainInstitute for Energy Engineering, Universitat Politècnica de València, Camino. de Vera s/n, 46022 Valencia, SpainInstitute for Energy Engineering, Universitat Politècnica de València, Camino. de Vera s/n, 46022 Valencia, SpainInstitute for Energy Engineering, Universitat Politècnica de València, Camino. de Vera s/n, 46022 Valencia, SpainInduction motors are essential components in industry due to their efficiency and cost-effectiveness. This study presents an innovative methodology for automatic fault detection by analyzing images generated from the Fourier spectra of current signals using deep learning techniques. A new preprocessing technique incorporating a distinctive background to enhance spectral feature learning is proposed, enabling the detection of four types of faults: healthy motor coupled to a generator with a broken bar (HGB), broken rotor bar (BRB), race bearing fault (RBF), and bearing ball fault (BBF). The dataset was generated from three-phase signals of an induction motor controlled by a Direct Torque Controller under various operating conditions (20–1500 rpm with 0–100% load), resulting in 4251 images. The model, based on a Visual Geometry Group (VGG) architecture with 19 layers, achieved an overall accuracy of 98%, with specific accuracies of 99% for RAF, 100% for BRB, 100% for RBF, and 95% for BBF. A new model interpretability was assessed using explainability techniques, which allowed for the identification of specific learning patterns. This analysis introduces a new approach by demonstrating how different convolutional blocks capture particular features: the first convolutional block captures signal shape, while the second identifies background features. Additionally, distinct convolutional layers were associated with each fault type: layer 9 for RAF, layer 13 for BRB, layer 16 for RBF, and layer 14 for BBF. This methodology offers a scalable solution for predictive maintenance in induction motors, effectively combining signal processing, computer vision, and explainability techniques.https://www.mdpi.com/1424-8220/25/2/471fault diagnosisinduction motorsspectral imagesdeep learningexplainabilitypredictive maintenance |
| spellingShingle | Kevin Barrera-Llanga Jordi Burriel-Valencia Angel Sapena-Bano Javier Martinez-Roman Fault Detection in Induction Machines Using Learning Models and Fourier Spectrum Image Analysis Sensors fault diagnosis induction motors spectral images deep learning explainability predictive maintenance |
| title | Fault Detection in Induction Machines Using Learning Models and Fourier Spectrum Image Analysis |
| title_full | Fault Detection in Induction Machines Using Learning Models and Fourier Spectrum Image Analysis |
| title_fullStr | Fault Detection in Induction Machines Using Learning Models and Fourier Spectrum Image Analysis |
| title_full_unstemmed | Fault Detection in Induction Machines Using Learning Models and Fourier Spectrum Image Analysis |
| title_short | Fault Detection in Induction Machines Using Learning Models and Fourier Spectrum Image Analysis |
| title_sort | fault detection in induction machines using learning models and fourier spectrum image analysis |
| topic | fault diagnosis induction motors spectral images deep learning explainability predictive maintenance |
| url | https://www.mdpi.com/1424-8220/25/2/471 |
| work_keys_str_mv | AT kevinbarrerallanga faultdetectionininductionmachinesusinglearningmodelsandfourierspectrumimageanalysis AT jordiburrielvalencia faultdetectionininductionmachinesusinglearningmodelsandfourierspectrumimageanalysis AT angelsapenabano faultdetectionininductionmachinesusinglearningmodelsandfourierspectrumimageanalysis AT javiermartinezroman faultdetectionininductionmachinesusinglearningmodelsandfourierspectrumimageanalysis |