Enhanced Fault Detection in High-Speed Train Bearings Using Empirical Mode Decomposition (EMD) and Kurtosis-Based IMF Selection: A Test Bench Approach
The critical operating conditions of high-speed trains (HSTs) increase the occurrence of mechanical faults, particularly in key components such as axle bearings. To enhance fault detection and prevention, our study begins with controlled experimental simulations performed on a dedicated test rig at...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | EPJ Web of Conferences |
| Online Access: | https://www.epj-conferences.org/articles/epjconf/pdf/2025/15/epjconf_cistem2024_05002.pdf |
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| _version_ | 1849420573425270784 |
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| author | Abtane Meryem Dahi Khalid Martinez Hervé Sedki Mohamed El Kimi Hicham Fernandes Borges Luciano |
| author_facet | Abtane Meryem Dahi Khalid Martinez Hervé Sedki Mohamed El Kimi Hicham Fernandes Borges Luciano |
| author_sort | Abtane Meryem |
| collection | DOAJ |
| description | The critical operating conditions of high-speed trains (HSTs) increase the occurrence of mechanical faults, particularly in key components such as axle bearings. To enhance fault detection and prevention, our study begins with controlled experimental simulations performed on a dedicated test rig at the Complex Systems and Interactions (CSI) Laboratory of Ecole Centrale Casablanca. This setup enables a systematic investigation of various types of bearing faults under well-defined conditions. The proposed methodology utilizes Empirical Mode Decomposition (EMD) to decompose vibration signals into Intrinsic Mode Functions (IMFs). A kurtosis- based selection criterion is then applied to identify the IMF that best highlights fault-related features. This approach enhances the precision of fault detection and the characterization of bearing defects. It demonstrates strong potential for improving diagnostic capabilities in both conventional rolling-element bearings and future applications involving axle bearing fault detection in high-speed rail systems. |
| format | Article |
| id | doaj-art-350d39ddcd274b66a58df3fb60ae26c0 |
| institution | Kabale University |
| issn | 2100-014X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Web of Conferences |
| spelling | doaj-art-350d39ddcd274b66a58df3fb60ae26c02025-08-20T03:31:42ZengEDP SciencesEPJ Web of Conferences2100-014X2025-01-013300500210.1051/epjconf/202533005002epjconf_cistem2024_05002Enhanced Fault Detection in High-Speed Train Bearings Using Empirical Mode Decomposition (EMD) and Kurtosis-Based IMF Selection: A Test Bench ApproachAbtane Meryem0Dahi Khalid1Martinez Hervé2Sedki Mohamed3El Kimi Hicham4Fernandes Borges Luciano5Complex Systems and Interactions, Ecole Centrale of CasablancaComplex Systems and Interactions, Ecole Centrale of CasablancaComplex Systems and Interactions, Ecole Centrale of CasablancaSociété Marocaine de Maintenance des Rames à Grande Vitesse (SIANA)Société Marocaine de Maintenance des Rames à Grande Vitesse (SIANA)Société Marocaine de Maintenance des Rames à Grande Vitesse (SIANA)The critical operating conditions of high-speed trains (HSTs) increase the occurrence of mechanical faults, particularly in key components such as axle bearings. To enhance fault detection and prevention, our study begins with controlled experimental simulations performed on a dedicated test rig at the Complex Systems and Interactions (CSI) Laboratory of Ecole Centrale Casablanca. This setup enables a systematic investigation of various types of bearing faults under well-defined conditions. The proposed methodology utilizes Empirical Mode Decomposition (EMD) to decompose vibration signals into Intrinsic Mode Functions (IMFs). A kurtosis- based selection criterion is then applied to identify the IMF that best highlights fault-related features. This approach enhances the precision of fault detection and the characterization of bearing defects. It demonstrates strong potential for improving diagnostic capabilities in both conventional rolling-element bearings and future applications involving axle bearing fault detection in high-speed rail systems.https://www.epj-conferences.org/articles/epjconf/pdf/2025/15/epjconf_cistem2024_05002.pdf |
| spellingShingle | Abtane Meryem Dahi Khalid Martinez Hervé Sedki Mohamed El Kimi Hicham Fernandes Borges Luciano Enhanced Fault Detection in High-Speed Train Bearings Using Empirical Mode Decomposition (EMD) and Kurtosis-Based IMF Selection: A Test Bench Approach EPJ Web of Conferences |
| title | Enhanced Fault Detection in High-Speed Train Bearings Using Empirical Mode Decomposition (EMD) and Kurtosis-Based IMF Selection: A Test Bench Approach |
| title_full | Enhanced Fault Detection in High-Speed Train Bearings Using Empirical Mode Decomposition (EMD) and Kurtosis-Based IMF Selection: A Test Bench Approach |
| title_fullStr | Enhanced Fault Detection in High-Speed Train Bearings Using Empirical Mode Decomposition (EMD) and Kurtosis-Based IMF Selection: A Test Bench Approach |
| title_full_unstemmed | Enhanced Fault Detection in High-Speed Train Bearings Using Empirical Mode Decomposition (EMD) and Kurtosis-Based IMF Selection: A Test Bench Approach |
| title_short | Enhanced Fault Detection in High-Speed Train Bearings Using Empirical Mode Decomposition (EMD) and Kurtosis-Based IMF Selection: A Test Bench Approach |
| title_sort | enhanced fault detection in high speed train bearings using empirical mode decomposition emd and kurtosis based imf selection a test bench approach |
| url | https://www.epj-conferences.org/articles/epjconf/pdf/2025/15/epjconf_cistem2024_05002.pdf |
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