Multi-Rate Vibration Signal Analysis for Bearing Fault Detection in Induction Machines Using Supervised Learning Classifiers
Vibration signals carry important information about the health state of a ball bearing and have proven their efficiency in training machine learning models for fault diagnosis. However, the sampling rate and frequency resolution of these acquired signals play a key role in the detection analysis. In...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/12/1/17 |
| _version_ | 1797343168396525568 |
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| author | Nada El Bouharrouti Daniel Morinigo-Sotelo Anouar Belahcen |
| author_facet | Nada El Bouharrouti Daniel Morinigo-Sotelo Anouar Belahcen |
| author_sort | Nada El Bouharrouti |
| collection | DOAJ |
| description | Vibration signals carry important information about the health state of a ball bearing and have proven their efficiency in training machine learning models for fault diagnosis. However, the sampling rate and frequency resolution of these acquired signals play a key role in the detection analysis. Industrial organizations often seek cost-effective and qualitative measurements, while reducing sensor resolution to optimize their resource allocation. This paper compares the performance of supervised learning classifiers for the fault detection of bearing faults in induction machines using vibration signals sampled at various frequencies. Three classes of algorithms are tested: linear models, tree-based models, and neural networks. These algorithms are trained and evaluated on vibration data collected experimentally and then downsampled to various intermediate levels of sampling, from 48 kHz to 1 kHz, using a fractional downsampling method. The study highlights the trade-off between fault detection accuracy and sampling frequency. It shows that, depending on the machine learning algorithm used, better training accuracies are not systematically achieved when training with vibration signals sampled at a relatively high frequency. |
| first_indexed | 2024-03-08T10:43:51Z |
| format | Article |
| id | doaj.art-eeff3370585a4cdcb92f2b3ab92ad4fb |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-08T10:43:51Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-eeff3370585a4cdcb92f2b3ab92ad4fb2024-01-26T17:23:39ZengMDPI AGMachines2075-17022023-12-011211710.3390/machines12010017Multi-Rate Vibration Signal Analysis for Bearing Fault Detection in Induction Machines Using Supervised Learning ClassifiersNada El Bouharrouti0Daniel Morinigo-Sotelo1Anouar Belahcen2Department of Electrical Engineering and Automation, Aalto University, 00076 Espoo, FinlandResearch Group ADIRE, ITAP Institute, Universidad de Valladolid, 47002 Valladolid, SpainDepartment of Electrical Engineering and Automation, Aalto University, 00076 Espoo, FinlandVibration signals carry important information about the health state of a ball bearing and have proven their efficiency in training machine learning models for fault diagnosis. However, the sampling rate and frequency resolution of these acquired signals play a key role in the detection analysis. Industrial organizations often seek cost-effective and qualitative measurements, while reducing sensor resolution to optimize their resource allocation. This paper compares the performance of supervised learning classifiers for the fault detection of bearing faults in induction machines using vibration signals sampled at various frequencies. Three classes of algorithms are tested: linear models, tree-based models, and neural networks. These algorithms are trained and evaluated on vibration data collected experimentally and then downsampled to various intermediate levels of sampling, from 48 kHz to 1 kHz, using a fractional downsampling method. The study highlights the trade-off between fault detection accuracy and sampling frequency. It shows that, depending on the machine learning algorithm used, better training accuracies are not systematically achieved when training with vibration signals sampled at a relatively high frequency.https://www.mdpi.com/2075-1702/12/1/17condition monitoringball bearingsampling frequencysupervised machine learningvibrations |
| spellingShingle | Nada El Bouharrouti Daniel Morinigo-Sotelo Anouar Belahcen Multi-Rate Vibration Signal Analysis for Bearing Fault Detection in Induction Machines Using Supervised Learning Classifiers Machines condition monitoring ball bearing sampling frequency supervised machine learning vibrations |
| title | Multi-Rate Vibration Signal Analysis for Bearing Fault Detection in Induction Machines Using Supervised Learning Classifiers |
| title_full | Multi-Rate Vibration Signal Analysis for Bearing Fault Detection in Induction Machines Using Supervised Learning Classifiers |
| title_fullStr | Multi-Rate Vibration Signal Analysis for Bearing Fault Detection in Induction Machines Using Supervised Learning Classifiers |
| title_full_unstemmed | Multi-Rate Vibration Signal Analysis for Bearing Fault Detection in Induction Machines Using Supervised Learning Classifiers |
| title_short | Multi-Rate Vibration Signal Analysis for Bearing Fault Detection in Induction Machines Using Supervised Learning Classifiers |
| title_sort | multi rate vibration signal analysis for bearing fault detection in induction machines using supervised learning classifiers |
| topic | condition monitoring ball bearing sampling frequency supervised machine learning vibrations |
| url | https://www.mdpi.com/2075-1702/12/1/17 |
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