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Residual life prediction method of rotation machinery under influence of health management behavior

A technology of health management and life prediction, which is applied in the direction of machine/structural component testing, measuring devices, complex mathematical operations, etc., and can solve problems such as unconsidered maintenance activities and performance degradation

Inactive Publication Date: 2019-03-01
GUANGDONG UNIV OF PETROCHEMICAL TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The existing remaining life prediction research based on degradation modeling does not consider the impact of maintenance activities, but only considers the degradation data of equipment within an update cycle, and realizes the remaining life prediction of laser generators and inertial platforms under this assumption
It can be seen that the existing degradation modeling and remaining life prediction problems are all based on the condition that the equipment degradation process is gradual, slow-changing and not affected by health management actions, and it is generally assumed that after the equipment is maintained, its performance degradation will resume. start

Method used

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  • Residual life prediction method of rotation machinery under influence of health management behavior
  • Residual life prediction method of rotation machinery under influence of health management behavior
  • Residual life prediction method of rotation machinery under influence of health management behavior

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Embodiment 1

[0021] The present invention aims to realize the prediction of the remaining life of the rotating machinery. Firstly, the state monitoring data (such as the monitoring data of the surface vibration of the casing and the vibration of the shaft) of the rotating machinery in actual operation is obtained by means of state monitoring, and the data that can reflect its health status are extracted. Based on performance degradation data (such as vibration amplitude data), a performance degradation model is established; based on this model, the first-arrival time distribution of the degradation process is solved, and then the remaining life distribution is predicted using the first-arrival time distribution;

[0022] In the above-mentioned embodiment, a stochastic degradation model considering the influence of health management behavior is also established, and the unknown parameters of the degradation model and the parameters in the maintenance intensity process are estimated to derive ...

Embodiment 2

[0025] The difference with embodiment 1 is:

[0026] According to the impact of health management behavior, the random degradation process of rotating machinery can be described by the following random process:

[0027]

[0028] in It is a composite Poisson process, describing the frequency of arrival of external health management behaviors, {Λ k , k≥1} is an independent and identically distributed random variable, describing the random impact of each health management behavior on the equipment degradation state;

[0029] For the stochastic degradation process above, the estimation of the unknown parameters in the degradation model consists of two parts: the parameters of the composite Poisson process, denoted as Ξ 1 ; other parameters, expressed as Ξ 2 ; Assume that the observed health management behavior arrival time data is and repair strength data for Based on this, it can be obtained by the method of maximum likelihood estimation that parameter Ξ 2 The estim...

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Abstract

The present invention discloses a residual life prediction method of rotation machinery under an influence of a health management behavior. The method concretely comprises the following content: health management behavior arrival process mathematical description and random degeneration modeling under the influence of the health management behavior; solution of the first arrival time probability distribution of the random process and the predicted residual life distribution; model unknown parameter estimation and residual life prediction result updating; according to the health management behavior influence, description of the random degeneration process of the rotation machinery through the following random process as shown in the description, wherein a parameter as shown in the description is a composite Poisson process, and description of the frequency of the external health management behavior arrival. The residual life prediction method of rotation machinery under the influence ofthe health management behavior comprehensively utilizes the performance monitoring data and operation information in the device operation process, quantificationally discloses the influence of the health management behavior on the residual life prediction result through the performance degeneration modeling of the device under the influence of the health management behavior and the residual life prediction method, and has the innovation of the research problem level.

Description

technical field [0001] The invention relates to the field of life calculation of mechanical equipment, in particular to a method for predicting the remaining life of rotating machinery under the influence of health management behaviors. Background technique [0002] With the large-scale development of the petrochemical industry, the rotating mechanical equipment in the large unit of the petrochemical industry tends to be large-scale, precise, high-speed and automated, and its composition and structure are becoming more and more complex. On the one hand, these developments have improved production efficiency and reduced production costs. On the other hand, they have put forward higher and stricter requirements for the design, manufacture, installation, use, maintenance and reliable operation of machinery. A small fault may cause the performance of the equipment to degrade, deteriorate or even fail, affect the stability and safety of the entire system operation, and even cause...

Claims

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Application Information

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IPC IPC(8): G01M99/00G06F17/18
CPCG01M99/005G06F17/18
Inventor 孙国玺司小胜张清华
Owner GUANGDONG UNIV OF PETROCHEMICAL TECH
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