Intelligent Condition Monitoring of a Ball Roller Bearing
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Date
2022-04-30
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Indiana Journal of Humanities and Social Sciences
Abstract
A bearing itself is one of the essential components that allow constrained relative motion between two or more parts, typically motion or linear movement. It is cylindrical in shape and contains small metal balls and has caused various problems in machineries. Common roller bearings use cylinders of slightly greater length than diameter. Roller bearings are therefore the earliest known type of rolling element bearing. Maintenance of rotary machinery is very essential in all walks of different industries and recently has attracted great attention. Methods of detecting and diagnosing the defective rolling element bearing. Numerous methods have been developed based on intelligent condition monitoring and in this case artificial neural networks, fuzzy expert system, condition based reasoning, vibration measurements, temperature measurements, shock pulse method have been applied and measured. Acceleration sensor was applied on a roller bearing with a charge amplifier to measure acoustic emissions. Analogue-Digital converter was used to produce signal pre-processing which lead to feature extraction. Time and frequency domains were used to show signals change over time and analysis of mathematical functions of signals respectively. Artificial Neural Networks and pattern recognition identifies defects. The computer uses mathematical laboratory software (MATLAB) for command plots of inputs and output signals and indicates a normal or a failing bearing..
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Chasokela, D., Muzari, T. and Tshuma, L.S., 2022. Intelligent Condition Monitoring of a Ball Roller Bearing. Indiana Journal of Humanities and Social Sciences, 3(4), pp.33-42.