Fault size diagnosis of rolling element bearing using artificial neural network and dimension theory

Failure of roller bearings can cause downtime or a complete shutdown of rotating machines. Therefore, a well-timed detection of bearing defects must be performed. Modern condition monitoring demands simple but effective bearing failure diagnosis by integrating dynamic models with intelligence techniques. This paper presents an integration of Dimensional Analysis (DA) and Artificial Neural Network (ANN) to diagnose the size of the bearing faults. The vibration responses of artificially damaged bearings using Electrode Discharge Machining are collected using Fast Fourier Techniques on a developed rotor-bearing test rig. Two-performance indicators, actual error, and performance of error are used to evaluate the accuracy of models. The simplicity of the DA model and the performance of the ANN model predicting with 5.49% actual error and 97.79 performance of error band enhanced the accuracy of diagnosis compared to the experimental results. Moreover, ANN has shown good performance over experimental results and DA.