Prediction of defect size in rolling element bearings using acoustic emission signals with an artificial neural network

Abstract

In the condition monitoring of rotating machinery, vibration analysis of rolling element bearings is a popular diagnostic tool even though the vibration signals caused by bearing defects are distorted by other faults and mechanical noise, particularly in a hostile environment. The acoustic emission (AE) method is a non-destructive testing (NDT) technique used in structural health monitoring and its application for bearing diagnosis is gaining momentum as an alternative diagnostic tool due to its inherent high signal-to-noise (SNR) ratio. In earlier studies, although the researchers focused on different types of seeded defects, with random shape and size, and measured their vibration amplitude, they could not ascertain the correlation between the defect size and its respective vibration amplitude. A bearing test-rig was designed and established to study the various size defects in rolling element bearings. The experimental investigation reported in this paper predicts the bearing damage severity with respect to the AE amplitude level, using the artificial neural network (ANN) technique. This experiment includes seeded defects of various sizes, ie gradual increase of defect width on the outer race of radially-loaded cylindrical roller bearings at different parameters, and the data acquired through an acoustic emission probe. Experimental data was imported to the ANN, in which a multilayer perception model was used with a back-propagation algorithm using the input parameters of load, r/min and AE amplitude level and defect size as the output. The predicted defect sizes are compared with the actual seeded defect sizes and the percentage error was minimal. In this paper, an attempt has been made to predict the defect size with the help of AE and ANN techniques.