<p>Accurate prediction of equipment's remaining life (RL) is of significant importance for enhancing operational reliability and reducing operational risks and maintenance costs. For equipment whose degradation trajectories simultaneously exhibit characteristics of both power-law and exponential functions, existing degradation modeling and RL prediction methods primarily rely on single nonlinear degradation models, either exponential or power-law. However, a single model struggles to fully describe this dual degradation characteristic, leading to substantial errors in RL prediction. To enhance the prediction accuracy of the remaining life (RL) for such equipment, this work introduces a novel class of nonlinear Wiener process-based degradation models integrating both power-law and exponential functions. Firstly, leveraging the properties of the Wiener process and the definition of the first passage time (FPT), analytical expressions for the probability density functions (PDFs) of both the lifetime and the RL for the proposed degradation model are derived. Secondly, the unknown parameters within the degradation model are estimated using the maximum likelihood estimation method. Finally, the proposed model is validated and analyzed through simulated data, gyroscopic drift data from an inertial navigation platform, and fatigue crack growth data from aluminum alloy. The results demonstrate that the newly proposed degradation model significantly improves the RL prediction accuracy for this category of equipment, reduces prediction errors, and demonstrates considerable practical value.</p>