Classification of acoustic emission signals generated from journal bearing at different lubrication conditions based on wavelet analysis in combination with artificial neural network and genetic algorithm

This paper presents the results of acoustic emission (AE) investigation for monitoring lubrication conditions of a journal bearing under various operating conditions. Hydrodynamic Lubrication (HL), Mixed Lubrication (ML), and Boundary Lubrication (BL), are the basic types of the fluid film lubrication. The aim of this investigation is to identify effective frequencies and most useful features of the AE signals for classification of the lubrication types. Continuous wavelet transform (CWT) and time domain signal analysis methods are used for feature extraction of the recorded AE signals. Then, Genetic Algorithms (GAs) in combination with Artificial Neural Networks (ANNs) are applied to select and classify the extracted features. The experimental results showed that the proposed system using AE signal is effective.