Equipment residual life prediction method under sequential Bayesian framework

Abstract

The invention discloses an equipment residual life prediction method under a sequential Bayesian framework, and the method comprises the steps: firstly constructing a degradation model of degraded equipment in a project through employing a Wiener process with a random drift coefficient; then, performing off-line estimation on hyper-parameters and diffusion coefficients in drift coefficients in the degradation model by using a maximum likelihood estimation method based on historical degradation data of similar equipment; on the basis of degradation data monitored in real time, achieving on-line recursion of a drift coefficient hyper-parameter under a sequential Bayesian framework; and finally, deriving an analytical expression of the residual life probability density function under the concept of the first arrival time. Different from existing research based on a Bayesian method, the sequential Bayesian method mainly takes a parameter updating result at a previous moment as prior distribution at a next moment. Therefore, the invention can make full use of the information contained in all the degradation data of the specific equipment up to the current moment, and overcomes the problem that a traditional Bayesian method only depends on the degradation data of the current moment.

Description

technical field [0001] The invention belongs to the technical field of reliability engineering, and in particular relates to a method for predicting remaining life of equipment under a sequential Bayesian framework. Background technique [0002] Over the past few decades, industrial equipment has grown in complexity and automation. For such a device, it is much more difficult to understand its health and predict its potential failure. This difficulty gave rise to an emerging concept, prognostics and health management (PHM), and drove its rapid development. PHM is generally considered to be an effective tool to evaluate the reliability of equipment under actual operating conditions and reduce operating costs or failure risks through some management activities. After the continuous exploration of scholars, the research of PHM technology has achieved a large number of theoretical results, which are widely used in various fields such as electronics, batteries, bearings, motor ...

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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that the traditional Bayesian method only depends on the current degradation level when updating the Wiener process drift coefficient. information

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