Strengthened change point detection model for weak mean difference data

摘要: Objective: The lifetime difference in adjacent parallel structure components becomes small as the number of components belonging to the same parallel structure increases. To infer the system structure, we must clarify the components that belong to the same parallel structure. Methods: A strengthened change point detection model (SCPDM) for weak mean difference data (WMDD) is established, which usually indicates that, as affected by a large variance, the mean difference in two subsignals for one data sequence becomes nonsignificant. For repeatedly retrievable WMDD, we performed two enhanced operations that doubled the mean difference by using the variance information and analyzed the asymptotic properties of the enhanced data. Then, we proposed an SCPDM based on the asymptotic results.Results: Finally, we compared the SCPDM with two other main change point detection models and verified that the SCPDM is superior to other models using WMDD change point detection by the simulation method.Limitations: This paper also have several limitations. First, we only discussed that are independent with normal distribution and single change point. Second, the reason why the relationship between and has an important influence on the accuracy of change point detection is not discussed in depth. We only defined the ratio boundary of WMDD by experience and simulation. Conclusions: Traditional change point detection models may become insensitive or ineffective for WMDD. We gave some asymptotic analysis and established a enhanced change point detection model (SCPDM) based on the asymptotic results. Compared with the traditional method, SCPDM can effectively detect the change point.