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- W3128613639 abstract "The modern world is ruled by machines and most of them have some sort of rotating part inside them which are often interconnected using a ball bearing. A great research effort is given towards the maintenance of these ball bearings as bearing faults account for about 65-70% of all machine failures. Ball bearings are a low-cost fix in a machine, but most of the time it's the hardest fault to detect. This study proposes a radial basis neural-network based detection method for such rolling element ball bearing faults. This would find out specific features about the ball bearing vibration signal and find patterns in them to predict bearing faults effectively. As most real-world signals are prone to noise, an adaptive denoising filter is used to denoise the signal. The network is optimized using a genetic algorithm that leads to the use of a lower amount to features without sacrificing any kind of performance. The algorithm used provides a high accuracy of 98.3% in the classification of rolling element ball bearing fault and also works very well on noisy signals up to -10db SNR. This solves the problem of detecting ball bearing fault at early stages. Also, as this is a neuralnetwork based approach, no manual checking is needed and this study proposes a reliable, fast and automated procedure of diagnostics." @default.
- W3128613639 created "2021-02-15" @default.
- W3128613639 creator A5050107740 @default.
- W3128613639 creator A5065155032 @default.
- W3128613639 date "2020-11-28" @default.
- W3128613639 modified "2023-09-23" @default.
- W3128613639 title "Classification of Bearing Fault Using Radial Basis Neural Network and Genetic Algorithm" @default.
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- W3128613639 doi "https://doi.org/10.1109/icaict51780.2020.9333527" @default.
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