A comparison of neural networks for FDI of rolling element bearings - demonstrated on experimental rig data

Samy, Ihab, Whidborne, James and Postlethwaite, Ian (2011) A comparison of neural networks for FDI of rolling element bearings - demonstrated on experimental rig data. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 225 (9). pp. 1012-1026. ISSN 0954-4100

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Abstract

In this article, a fault detection and isolation (FDI) approach for bearing faults in rotating machinery using a combination of vibration analysis and expert systems via neural networks (NNs) is proposed. The NN chosen is the extended minimum resource allocating network (EMRAN) radial basis function (RBF) due to its good performance characteristics. While the EMRAN RBF NN structure is itself not novel, the application to bearing FDI has, to the author's knowledge, been less frequently explored. The EMRAN RBF NN is used for pattern classification of four types of bearing health conditions: healthy, inner race, outer race, and ball bearing faults. A machine fault simulator is used to simulate the bearing faults and the input nodes of the NN include five features extracted from the time-domain vibration data: peak, root mean square, standard deviation, kurtosis, and normal negative log-likelihood value. Using real experimental data from a machine fault simulator, it was found that a 3-7-4 EMRAN RBF NN structure outperforms a 5-20-4 multilayered perceptron NN with zero false alarms, fewer undetected faults, higher pattern correlation factors, and faster execution times.

Item Type:	Article
Uncontrolled Keywords:	fault detection and isolation, neural networks, rotating machinery, pattern classification, expert systems
Subjects:	H400 Aerospace Engineering
Department:	Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User:	Ellen Cole
Date Deposited:	13 Dec 2012 14:41
Last Modified:	10 Oct 2019 22:30
URI:	http://nrl.northumbria.ac.uk/id/eprint/10621

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