Identification of wear and misalignment on journal bearings using artificial neural networks

Abstract

Journal bearing bushing wear and shaft misalignment are the common defects observed in rotating systems. Both defects influence their dynamic behaviour, stability and life time and their detection is a very important task. This article introduces a defect diagnosis approach that uses artificial neural networks. Reynolds equation is solved by finite element method and wear/misalignment data are provided. Then, the proposed model uses values of journal bearing performance characteristics, such as eccentricity, attitude angle and film thickness, and it trains an artificial neural network so that reliable defect identification is performed. The accuracy of the proposed model is demonstrated – for different misalignment angles, wear depths and L/ D ratios – for a worn/misaligned journal bearing. The potential use of the artificial neural networks-based model for a real-time condition monitoring system is also discussed. The outcome of this study is to obtain an efficient engineering tool that would be capable of detecting wear and misalignment of journal bearings for different operating conditions using artificial neural networks.