A Lifetime Prediction Method Based on Change Point Detection of Correlated Synchronized Time Series Signals

Abstract

The invention discloses a life prediction method based on the change point detection of an associated synchronous time series signal, which is used to solve the life prediction problem under the interactive influence of large time scale and multi-dimensional time series. First, the original multi-dimensional synchronous time series is resampled to obtain the equivalent time series signal after scale reduction; secondly, a likelihood function is constructed for the sampled signal with the change point vector, piecewise slope and piecewise variance as parameters ; Then, for different values ​​of the number of change points, the maximum likelihood estimation is used to estimate the best value of the change point vector, piecewise slope and piecewise variance; The change point vector corresponding to the selected optimal number of change points divides the original time series into a plurality of local time series that are not constant zero and have only one change point, and repeatedly construct the likelihood function and In the step of optimal change point estimation, the final change point position of the original time series is obtained; finally, the signal at which the final change point is located is determined, and the time series at the tail of each dimension signal is obtained, which is used for parameter estimation required for subsequent life prediction.

Description

technical field [0001] Aiming at the problem of life prediction under the interactive influence of large time scale and multi-dimensional time series, the invention proposes a life prediction method based on change point detection of correlated synchronous time series signals, which belongs to the field of system engineering life prediction. Background technique [0002] The life prediction and system modeling of multi-component systems are the key issues in the reliability assurance of complex systems. The difficulty lies in the diverse dependencies between components and subsystems in the system due to structural or task associations, which make them fail. There is a strong correlation with degradation, and it is difficult to rely on independent mathematical or physical models to express, so it is difficult to complete accurate system-level life prediction. Therefore, in the system-wide life prediction and health status monitoring tasks, how to consider and process the rel...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the problem of life prediction under the interactive influence of large time scale and multi-dimensional time series, and propose a life prediction method based on the change point detection of correlated synchronous time series signals

Images