Empirical Feature-Based Fault Diagnosis of Rolling Bearings with Coupled Defects Using Improved OAA-MCSVM

Abstract Accurate diagnosis of outer race defects and their interaction with secondary faults remains a critical challenge in bearing condition monitoring. This study presents a physics-based diagnostic approach that integrates extended hamilton's principle (EHP) with an improved one-against-all multiclass support vector machine (OAA-MCSVM) for identifying and classifying complex bearing faults. A dynamic model of the rotor-bearing system is developed using EHP to capture the influence of outer race defects under combined fault scenarios, including misalignment, unbalance, and radial clearance variation. Vibration responses are acquired from a controlled test rig under isolated and coupled fault conditions. Fault signatures are extracted through time-frequency analysis and mapped to system dynamics derived from the variational formulation. The extracted features are classified using the improved OAA-MCSVM framework, which enhances boundary discrimination between closely interacting faults. Experimental validation shows that the proposed method achieves high classification accuracy across all tested fault conditions, with improved sensitivity to outer race-related compound faults. The integration of physics-based modeling with machine learning enables a more interpretable and reliable fault diagnosis scheme, suitable for real-time application in rotating machinery.