This paper aims to improve the performance and speed of artificial neural network (ANN)‐ball‐bearing fault detection expert systems by eliminating unimportant inputs and changing the ANN structure.
An algorithm is used to select the best subset of features to boost the success of detecting healthy and faulty ball. Some of the important parameters of the ANN are also optimized to make the classifier achieve the maximum performance.
It was found that better accuracy can be obtained for ANN with fewer inputs.
The method can be used for other machinery condition‐monitoring systems which are based on ANN.
The results are useful for bearing fault detection systems designers and quality check centers in bearing manufacturing companies.
The algorithm used in this research is faster than in previous studies. Changing ANN parameters improved the results. The system was examined using experimental data of ball‐bearings.
Hajnayeb, A., Khadem, S.E. and Moradi, M.H. (2008), "Design and implementation of an automatic condition‐monitoring expert system for ball‐bearing fault detection", Industrial Lubrication and Tribology, Vol. 60 No. 2, pp. 93-100. https://doi.org/10.1108/00368790810858395
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