Long-range correlation degradation process remaining life prediction method depending on time and states

Abstract

The invention discloses a long-range correlation degradation process remaining life prediction method depending on time and states, and belongs to the technical field of health management. The method comprises the following steps that firstly, sensor data sampled at equal intervals is acquired; a degeneration model is established on the basis of the fractional Brownian motion according to degradation data characteristics; a Hurst index in the model is established by utilizing a quadratic-variation-based method; drift term unknown parameters are estimated by utilizing a likelihood ratio function to the largest extent, wherein the likelihood ratio function is constructed through a Radon-Nikodym derivative; diffusion term unknown parameters are estimated through the maximum likelihood method; then by means of the weak convergence theory, an original degradation process is approximated to a random process which has a time-varying diffusion term coefficient and is based on the Brownian motion; the degradation process is further simplified through a group of transformation; finally, analyzed remaining life distribution is obtained. By means of the method, the remaining life distribution can be accurately predicted.

Description

technical field [0001] The invention belongs to the technical field of health management, in particular to a method for predicting the remaining life of a long-range correlation degradation process dependent on time and state. Background technique [0002] The normal operation of industrial equipment is the fundamental premise to ensure production safety and enterprise benefits. Once industrial equipment fails, it will not only bring huge economic losses to production, but may also cause serious accidents. Therefore, health management is of great significance to industrial equipment. Remaining life prediction is an important part of health management. Accurate remaining life prediction results can effectively guide equipment maintenance strategies and spare parts supply, thereby avoiding waste caused by excessive maintenance and losses caused by failures. [0003] In order to obtain accurate life prediction results, it is necessary to establish a model that can describe e...

AI Technical Summary

Problems solved by technology

Although there are currently a small number of works focusing on modeling both time- and state-dependent degradation processes and long-range dependent degradation processes, none of them can simultaneously consider time- and state-dependent long-range dependent degradation process modeling and residual Life Prediction Methods

[0004] The remaining lifetime prediction of time- and state-dependent long-range correlation degradation processes mainly has the following difficulties: First, since the degradation state itself is included in the degradation model, it is impossible to write an analytical likelihood function, and it is difficult to use traditional estimation methods for parameter estimation

Second, since the degenerate process is neither a Markov process nor a semi-martingale, it is difficult to obtain the analytical first-arrival time distribution

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