Degradation modeling-based equipment failure number prediction method

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...

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 ful

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