Thrust ball bearing fault identification from visualized data using optimized deep network

Abstract

Bearings are the most common components employed in machine parts. The bearings' movement facilitates the smooth motion. They also help with friction reduction. The faults in bearings are often caused by tribological parameters. Various methods have been developed to identify faults in bearings, but they often fail to predict these accurately. This work has concentrated on designing an effective fault-recognizing model. Therefore, a framework, the Zebra-based Radial Basis Prediction Mechanism (ZbRBPM), was proposed in this work. Initially, the bearing datasets are collected, and mineral oil (MO) lubrication is added to minimize wear and friction. The primary goal of the work is to detect and classify the fault in the thrust ball bearing. The bearing vibration data included a normal vibration signal, a ball fault, and inner and outer race faults. Hence, Zebra fitness is enhanced for the optimization of tribological parameters and fault identification. The proposed model is executed in the MATLAB system. Finally, performance criteria like accuracy, precision, recall, F-score, error rate, computation time, speed, specific wear rate, friction torque, and energy consumption are validated. The performance of the proposed ZbRBPM gains better accuracy rate as 99.5 %, 99 % of precision and f-score and 99.4 % of recall. Also it significantly reduced the prediction error rate into 0.5 % with lower computation time and very low wear and friction.