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

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.