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Fault Detection in Gearboxes Using Fisher Criterion and Adaptive Neuro-Fuzzy Inference

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Pubblicato in:Machines vol. 13, no. 6 (2025), p. 447-471
Autore principale: Habbouche Houssem
Altri autori: Benkedjouh Tarak, Amirat Yassine, Benbouzid Mohamed
Pubblicazione:
MDPI AG
Soggetti:
Classism
Feature extraction
Accuracy
Datasets
Wavelet transforms
Fuzzy logic
Time domain analysis
Feature selection
Mechanical systems
Mechanical drives
Automation
Localization
Fault detection
Stress concentration
Performance evaluation
Computer simulation
Gearboxes
Data analysis
Adaptive systems
Principal components analysis
Fault diagnosis
Reliability
Genetic algorithms
Classification
Inference
Power transmission
Criteria
Lubricants & lubrication
Shutdowns
Methods
Artificial intelligence
Degrees of freedom
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Abstract: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.
ISSN:2075-1702
DOI:10.3390/machines13060447
Fonte:Engineering Database