Vibration Characteristics Diagnosis and Estimation of Fault Sizes in Rolling Contact Bearings: A Model-Based Approach

Abstract

A novel model-based technique is presented in this paper for the estimation of the fault size in different components of rolling contact bearings. A detailed dimensional analysis of the problem is carried out and an experimental methodology using the Box–Behnken design is applied to generate the experimental data set. First, the analysis of the vibration acceleration amplitude at fault frequency, its dependence on the bearing operating, and fault parameters using the obtained vibration data set are carried out by statistical analysis of variance. Numerical equations are developed then using the experimental data set for the correlation of the vibration acceleration amplitude in the frequency domain with the fault sizes based on the developed dimensionless terms. A hybrid backpropagation neural network integrating genetic algorithm is also developed to check the computational performance of the developed model equations. Validation of the proposed method is carried experimentally also for three seeded defect sizes on the outer race, inner race, and rolling element. The maximum model accuracy observed is for the inner race defect case with a predictive accuracy of 99.44% and for the roller defect case, it is 98.77%. The deviance observed for the model predictive performance is maximum for the outer race defect case with the least accuracy of 90.47% amongst all.