With more and more attention on degradation process, we need the degradation model to be accurate all over the time rather than only at some specific moments. However, the traditional validation metric only estimates difference of static features. A validation method proposed in this paper uses hypothesis testing to identify whether the distributions of experimental measurements and simulation results are consistent. Then, based on the deviation between sample means, a global validation metric which reflects the difference of degradation process between computational model and physical system all over the service time is derived from the statistics of deviation between sample means. Furthermore, curve fit method for discrete experimental measurements is introduced. The case of electro-hydraulic servo valve is studied, and the results show that the proposed validation metric is appropriate for the validation of degradation model with dynamic performance output. With more and more attention on degradation process, we need the degradation model to be accurate all over the time rather than only at some specific moments. However, the traditional validation metric only estimates difference of static features. A validation method proposed in this paper uses hypothesis testing to identify whether the distributions of experimental measurements and simulation results are consistent. Then, based on the deviation between sample means, a global validation metric which reflects the difference of degradation process between computational model and physical system all over the service time is derived The case of electro-hydraulic servo valve is studied, and the results show that the proposed validation metric is appropriate for the validation of degradation model with dynamic performance output.