Predictive simulation of ball bearing faults: A dual-environment study using analytical and CAD-based multi-body dynamics models

Ball bearings are vital elements in rotary machinery, and their premature failure can cause severe operational disruptions and unplanned downtime. The present work proposes a dual-model framework for simulating and analysing bearing faults to support the predictive maintenance strategies. The first model is a physics-based seven degrees of freedom model developed in MATLAB-SIMULINK environment, and the second is a detailed multibody dynamics model built in MSC ADAMS, providing a physically grounded, high-fidelity counterpart to the physics-based model. Both models are deliberately seeded with the most frequently encountered bearing faults, allowing for comprehensive virtual testing. To validate the simulation results, controlled experiments are conducted using a machine-fault simulator. The results of both models are analysed qualitatively in time, frequency and time–frequency domains. To quantify similarity between the models and experiments, the Pearson correlation coefficients for the envelope spectra indicate strong agreement between the MSC ADAMS multibody model and the experimental responses. However, both models successfully extract most of the fault-related symptoms, except in the ball fault case, where the MSC ADAMS model's response is more closely aligned with the experiments.