Machine Learning in Crack Size Estimation of a Spur Gear Pair Using Simulated Vibration Data
| Autor: | Stephen Ekwaro-Osire, Abdul Serwadda, Abraham Nispel, Ozhan Gecgel, Fisseha M. Alemayehu, João Paulo Dias |
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| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
business.product_category business.industry Computer science Decision tree learning Feature extraction Condition monitoring 02 engineering and technology Machine learning computer.software_genre Fault (power engineering) Power (physics) Vibration 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Fracture (geology) 020201 artificial intelligence & image processing Artificial intelligence business computer Pinion |
| Zdroj: | Mechanisms and Machine Science ISBN: 9783319992679 |
| Popis: | Gears are the main components of power transmissions and are subjected to high cyclic load regime which can lead to premature fracture of the gear teeth. In order to prevent such events, research on gear condition monitoring and fault diagnostics techniques have received considerable attention. Machine learning (ML) applications have been widely combined with vibration measurement and analysis techniques for fault diagnostics in gearboxes and the majority of current techniques rely on experiments to generate training data. Despite the recognized advantages of using simulated data to train ML classifiers, this approach is still not a widespread practice. This paper proposes a simulation-driven ML framework to estimate the crack size in a gear tooth using simulated vibrations signals. Firstly, a 6-degrees-of-freedom model of a one-stage gearbox was developed to simulate the dynamic behavior of a cracked pinion. Secondly, a sample with 900 simulated vibration signals was generated considering 4 different crack sizes in the pinion tooth. Thirdly, the features of the vibration signals were extracted using 20 statistical indicators and, then, the extracted features were used to train and test 4 machine learning classifiers. Several performance evaluation metrics were computed, and the performance of the ML classifiers was compared and discussed. It was shown that the Decision Tree Classifier (DTC) performed best in the identification of small crack sizes regardless of the random selection of the training subsample. |
| Databáze: | OpenAIRE |
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