Fault Detection in Gearboxes Using Fisher Criterion and Adaptive Neuro-Fuzzy Inference
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| Publicado en: | Machines vol. 13, no. 6 (2025), p. 447-471 |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2075-1702 | ||
| 024 | 7 | |a 10.3390/machines13060447 |2 doi | |
| 035 | |a 3223924863 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231531 |2 nlm | ||
| 100 | 1 | |a Habbouche Houssem |u Mechanical Structures Laboratory, Ecole Militaire Polytechnique, Algiers 16046, Algeria; houssem.habbouche@emp.mdn.dz (H.H.); tarak.benkedjouh@emp.mdn.dz (T.B.) | |
| 245 | 1 | |a Fault Detection in Gearboxes Using Fisher Criterion and Adaptive Neuro-Fuzzy Inference | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Gearboxes are autonomous devices essential for power transmission in various mechanical systems. When a failure occurs, it can lead to an inability to perform the required functions, potentially resulting in a complete shutdown of the mechanism and causing significant operational disruptions. Consequently, deploying expert methods for fault detection and diagnosis is crucial to ensuring the reliability and efficiency of these systems. Artificial intelligence (AI) techniques show promise for fault diagnosis, but their accuracy can be hindered by noise and manufacturing imperfections that distort mechanical signatures. Thorough data analysis and preprocessing are vital to preserving these critical features. Validating approaches through numerical simulations before experimentation is essential to identify model limitations and minimize risks. A hybrid approach, combining AI and physics-based models, could provide a robust solution by leveraging the strengths of both domains: AI for its ability to process large volumes of data and physics-based models for their reliability in modeling complex mechanical behaviors. This paper proposes a comprehensive diagnostic methodology. It starts with feature extraction from time-domain analysis, which helps identify critical indicators of gearbox performance. Following this, a feature selection process is applied using the Fisher criterion, which ensures that only the most relevant features are retained for further analysis. These selected features are then employed to train an Adaptive Neuro-Fuzzy Inference System (ANFIS), a sophisticated approach that combines the learning capabilities of neural networks with the reasoning abilities of fuzzy logic. The proposed methodology is evaluated using a dataset of gear faults generated through energy simulations based on a six-degree-of-freedom (6-DOF) model, followed by a secondary validation on an experimental dataset. | |
| 653 | |a Classism | ||
| 653 | |a Feature extraction | ||
| 653 | |a Accuracy | ||
| 653 | |a Datasets | ||
| 653 | |a Wavelet transforms | ||
| 653 | |a Fuzzy logic | ||
| 653 | |a Time domain analysis | ||
| 653 | |a Feature selection | ||
| 653 | |a Mechanical systems | ||
| 653 | |a Mechanical drives | ||
| 653 | |a Automation | ||
| 653 | |a Localization | ||
| 653 | |a Fault detection | ||
| 653 | |a Stress concentration | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Computer simulation | ||
| 653 | |a Gearboxes | ||
| 653 | |a Data analysis | ||
| 653 | |a Adaptive systems | ||
| 653 | |a Principal components analysis | ||
| 653 | |a Fault diagnosis | ||
| 653 | |a Reliability | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Classification | ||
| 653 | |a Inference | ||
| 653 | |a Power transmission | ||
| 653 | |a Criteria | ||
| 653 | |a Lubricants & lubrication | ||
| 653 | |a Shutdowns | ||
| 653 | |a Methods | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Degrees of freedom | ||
| 700 | 1 | |a Benkedjouh Tarak |u Mechanical Structures Laboratory, Ecole Militaire Polytechnique, Algiers 16046, Algeria; houssem.habbouche@emp.mdn.dz (H.H.); tarak.benkedjouh@emp.mdn.dz (T.B.) | |
| 700 | 1 | |a Amirat Yassine |u LabISEN, ISEN Yncrea Ouest, 29200 Brest, France; yassine.amirat@isen-ouest.yncrea.fr | |
| 700 | 1 | |a Benbouzid Mohamed |u Institut de Recherche Dupuy de Lôme (UMR CNRS 6027), University of Brest, 29238 Brest, France | |
| 773 | 0 | |t Machines |g vol. 13, no. 6 (2025), p. 447-471 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3223924863/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3223924863/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3223924863/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |