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Degradation modeling-based equipment failure number prediction method

A technology for equipment failure and prediction methods, which is applied in prediction, data processing applications, calculations, etc., and can solve the problems of ignoring the operating state of the equipment and ignoring the actual operating state of the equipment.

Active Publication Date: 2014-05-14
PLA SECOND ARTILLERY ENGINEERING UNIVERSITY
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  • Claims
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AI Technical Summary

Problems solved by technology

[0003] In view of the above-mentioned existing technical conditions, the purpose of the present invention is to provide a method for predicting the number of equipment failures based on degradation modeling, so as to solve the traditional problem of ignoring the actual operating state of the equipment when predicting the number of equipment failures based on failure data
[0005] The basic idea of ​​the present invention is to make full use of the performance degradation data obtained during the performance change process of the product, scientifically predict the remaining life of the individual high-reliability product, and determine the number of equipment failures on this basis to solve the traditional problem of equipment failure based on failure data. The problem of ignoring the actual operating state of the equipment when predicting the number of failures

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Embodiment

[0048]The method for predicting the number of equipment failures based on degradation modeling of the present invention comprises the following steps:

[0049] Step 1: Establish a performance degradation model of the equipment

[0050] The degradation model based on the Wiener process is a typical linear stochastic model describing the stochastic degradation process of equipment, that is, the performance degradation rate of equipment can be approximated as a constant. According to formula (1), describe the stochastic degradation process {X(t), t≥0} based on the Wiener process;

[0051] Step 2: Estimation of parameters in the performance degradation model

[0052] According to formula (2), (3) estimated at t k The maximum likelihood values ​​of time λ and σ;

[0053] Depend on figure 2 Visible, adopt the method of the present invention, can well predict the gyroscope degradation trend;

[0054] Step 3: Remaining Life Prediction

[0055] According to the formula (4) and (...

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Abstract

The invention relates to a degradation modeling-based equipment failure number prediction method. Performance degradation data obtained by a product during a performance changing process is fully utilized to predict the residual life of the single high-reliability product; a failure number of the equipment is determined; and spare part management is optimized based on the prediction of the failure number of the equipment. The method comprises the following four steps: establishing a performance degradation model of the equipment; carrying out parameter estimation in the performance degradation model; carrying out residual life prediction; and carrying out equipment failure number prediction. Compared with the prior art, the provided method enables prediction parsing to be carried out on the individual residual lift and the general reliability service life of the product; and the provided method can be used as an effective analytic tool for predicting the product residual life and the failure number. And a powerful theoretical basis and technical support can be provided for product maintenance and spare parts ordering strategy determination. Therefore, the expenditure can be saved and unnecessary economic loss can be avoided. And thus the method has the great engineering application value.

Description

technical field [0001] The invention belongs to the technical field of reliability engineering and relates to a method for predicting the number of equipment failures based on degradation modeling. Background technique [0002] The current research on spare parts management is based on the prediction of the number of equipment failures, and then the optimal inventory management is carried out under the condition that the prediction has been made, so as to improve the operation reliability of the equipment. But the inaccurate prediction is a common problem. However, for large As far as most equipment is concerned, the demand for spare parts is intermittent. One piece may not be needed for a long time, but several pieces may be needed at a time. This brings great difficulties to the prediction based on historical consumption. It is worth noting that , most of the previous studies have ignored an important causal relationship: the demand for spare parts is due to equipment fail...

Claims

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Application Information

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IPC IPC(8): G06Q10/04
Inventor 胡昌华司小胜何华锋周涛蔡光斌张建勋
Owner PLA SECOND ARTILLERY ENGINEERING UNIVERSITY
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