Accurate remaining useful life (RUL) prediction for critical components of nuclear power equipment is animportant way to realize aging management of nuclear power equipment. The electric gate valve is oneof the most safety-critical and widely distributed mechanical equipment in nuclear power installations. However, the electric gate valve's extended service in nuclear installations causes aging and degradationinduced by crack propagation and leakages. Hence, it is necessary to develop a robust RUL predictionmethod to evaluate its operating state. Although the particle filter(PF) algorithm and its variants can dealwith this nonlinear problem effectively, they suffer from severe particle degeneracy and depletion, whichleads to its sub-optimal performance. In this study, we combined the whale algorithm with regularizedparticle filtering(RPF) to rationalize the particle distribution before resampling, so as to solve theproblem of particle degradation, and for valve RUL prediction. The valve's crack propagation is studiedusing the RPF approach, which takes the Paris Law as a condition function. The crack growth is observedand updated using the root-mean-square (RMS) signal collected from the acoustic emission sensor. Atthe same time, the proposed method is compared with other optimization algorithms, such as particleswarm optimization algorithm, and verified by the realistic valve aging experimental data. The conclusion shows that the proposed method can effectively predict and analyze the typical valve degradationpatterns