Fault diagnosis of rolling element bearings using an EMRAN RBF neural network- demonstrated using real experimental data

Rolling element bearings are critical components of rotating machinery. Failure diagnosis of bearing faults is necessary and can often avoid more catastrophic failure consequences. Nowadays vibration condition monitoring is the most frequently used failure diagnostic method for rotating machinery. Several designs have been proposed in the literature and in this paper we propose a different approach using a radial basis function (RBF) neural network (NN) trained with extended minimum resource allocating network (EMRAN) algorithms, for pattern classification of 4 types of bearing health conditions: healthy, inner race, outer race and ball bearing faults. The input nodes of the NN consist of five features extracted from the time domain vibration data: peak, root mean square, standard deviation, kurtosis and normal negative log-likelihood value. Furthermore the NN is analyzed in terms of sensitivity to the different input features in order to remove significant and/or redundant inputs. The accuracy of the pattern classification technique is compared for both longitudinal and vertical accelerations. Using real experimental data from a machine fault simulator it was found that the EMRAN RBF NN requires only a few features and classifies the 4 types of bearing faults with good accuracy. The effectiveness of the approach proposed in this paper has illustrated its feasibility for real time condition monitoring of rotating machinery.