A nonlinear fatigue damage accumulation model for rolling bearing life prediction considering coupled load-variation effects

In practical engineering applications, rolling bearings and other critical components are typically subjected to complex, variable loading conditions. The coupled effects of load magnitude, frequency, and phase significantly accelerate the initiation and propagation of fatigue cracks. Although existing fatigue damage accumulation models partially account for load sequence and interaction, many of these models are overly complex and involve numerous parameters, making it challenging to strike a balance between accuracy and computational efficiency. To address this issue, this paper proposes a fatigue damage accumulation model based on nonlinear damage evolution theory, which simultaneously considers the effects of load interaction and material parameters under variable loading conditions. By incorporating the interaction factor and critical material parameters, the model more accurately characterizes the variations in load spectra and the differences in fatigue performance among different materials. Subsequently, the model was validated against cyclic loading test data for 16Mn steel, hot-rolled 16Mn steel, 30NiCrMoV12 steel, Ti–6Al–4V titanium alloy, GS-61 steel, Al2024–T42 aluminum alloy, C45 steel, Q235B steel, and Al6082–T6 aluminum alloy. Comparative analyses with the Miner rule, Manson–Halford model, Aeran's model, and its improved model demonstrated that the proposed model exhibits significant improvements in both predictive accuracy and generalization capability. Furthermore, to verify the model's applicability in real-world engineering environments, two rolling bearings subjected to variable operating conditions were selected for case studies. The results indicate that the model exhibits strong validity and applicability in fatigue life prediction, offering novel insights and methods for the safety assessment and life prediction of critical components subjected to complex loading spectra.