An Analysis of the Influence of Surface Roughness and Clearance on the Dynamic Behavior of Deep Groove Ball Bearings Using Artificial Neural Networks

The deep groove ball bearing is one of the most important components of the rotary motion system and is the research subject in this paper. After factory assembly, new ball bearings need to pass quality control. The conventional approach relies on measuring the vibration amplitudes for each unit and...

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Vydáno v:Materials Ročník 16; číslo 9; s. 3529
Hlavní autoři: Knežević, Ivan, Rackov, Milan, Kanović, Željko, Buljević, Anja, Antić, Aco, Tica, Milan, Živković, Aleksandar
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 04.05.2023
MDPI
Témata:
Algorithms
Analysis
Artificial intelligence
Artificial neural networks
Back propagation networks
Ball bearings
Clearances
Control algorithms
Digitization
Fault diagnosis
Grooves
Machine learning
Motion systems
Multilayer perceptrons
Neural networks
Quality control
Quality management
Signal processing
Surface roughness
Velocity
Vibration
Vibration analysis
Vibration measurement
clearance
vibration
neural network
bearing
surface roughness
ISSN:1996-1944, 1996-1944
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Shrnutí:The deep groove ball bearing is one of the most important components of the rotary motion system and is the research subject in this paper. After factory assembly, new ball bearings need to pass quality control. The conventional approach relies on measuring the vibration amplitudes for each unit and sorting them into classes according to the vibration level. In this paper, based on experimental research, models are created to predict the vibration class and analyze the dynamic behavior of new ball bearings. The models are based on artificial neural networks. A feedforward multilayer perceptron (MLP) was applied, and a backpropagation learning algorithm was used. A specific method of training groups of artificial neural networks was applied, where each network provided an answer to the input within the group, and the final answer was the mean value of the answers of all networks in the group. The models achieved a prediction accuracy of over 90%. The main aim of the research was to construct models that are able to predict the vibration class of a new ball bearing based on the geometric parameters of the bearing rings. The models are also applied to analyze the influence of surface roughness of the raceways and the internal radial clearance on bearing vibrations.
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ISSN:1996-1944
1996-1944
DOI:10.3390/ma16093529