Abstract: The testing and operation environment may be essentially different, and thus the fault detection rate of testing is different from that of the operation phase. Based on the G-O model, the representative of non-homogeneous Poisson process (NHPP), the fault detection rate from testing to operation is transformed considering the differences of profile of these two phases, and then a more precise NHPP model (TO-SRGM) considering the differences of fault intensity of testing and operation phases is obtained. Finally, the unknown parameters are estimated by the least-squares method based on normalized data set. Experiments show that the goodness-of-fit of the TO-SRGM is better than those of the G-O model and PZ-SRGM on a data set.