Detection of Compound Faults in Ball Bearings Using Multiscale-SinGAN, Heat Transfer Search Optimization, and Extreme Learning Machine
نویسندگان
چکیده
Intelligent fault diagnosis gives timely information about the condition of mechanical components. Since rolling element bearings are often used as rotating equipment parts, it is crucial to identify and detect bearing faults. When there several defects in components or machines, early detection becomes necessary avoid catastrophic failure. This work suggests a novel approach reliably identifying compound faults when availability experimental data limited. Vibration signals recorded from single ball consisting faults, i.e., inner race, outer elements with variation rotational speed. The measured vibration pre-processed using Hilbert–Huang transform, and, afterward, Kurtogram generated. multiscale-SinGAN model adapted generate additional images effectively train machine-learning models. To relevant features, metaheuristic optimization algorithms such teaching–learning-based optimization, Heat Transfer Search applied feature vectors. Finally, selected features fed into three models for identifications. results demonstrate that extreme learning machines can 100% Ten-fold cross-validation accuracy. In contrast, minimum ten-fold accuracy 98.96% observed support vector machines.
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ژورنال
عنوان ژورنال: Machines
سال: 2022
ISSN: ['2075-1702']
DOI: https://doi.org/10.3390/machines11010029