Roller Bearing Faults Classification Using Artificial Neural Network Based on Servo System with Observer Design

Abstract

The roller bearing is the main component of rotating machines, which is used to reduce friction while the machine operation. The bearing faults are the key problem of the rotating machine because they affect the unusual operation and caused machine downtime. This paper presented the fault detection approach based on an Artificial Neural Network (ANN) to recognize the bearing conditions. Servo systems with observers were designed for motor control and estimating current signal. The bearing conditions demonstrated in three cases consisted of normal, no lubricant, and outer race defect. For ANN model training, four statistical parameters including mean, crest factor, kurtosis, and root-mean-square (RMS) were selected to identify the causal characteristics of the motor current from observer and observation error data. the result indicated that the fault detection model has been displayed a classification accuracy of 94.4% which appropriate using in real operations.