A Prediction Method for Imperfect Maintenance of Remaining Life of Equipment Based on Indirect Monitoring Data

Abstract

The invention discloses a method for predicting the imperfect maintenance of remaining life of equipment based on indirect monitoring data, comprising the following steps: analyzing the physical structure of the equipment, identifying key parameters that can describe the degradation process of the equipment; collecting and monitoring the key parameters Parameter degradation data; use the collected data to estimate the parameters of the degradation model; according to the estimated parameter values, establish a prediction model for the imperfect maintenance of the remaining life of the equipment based on indirect monitoring data; according to the established prediction model, use the collected degradation data to Prediction of remaining life of equipment. Compared with the traditional method, the present invention reduces the economic cost of the test, shortens the research period, and can reflect the influence of the environment on the equipment more abundantly and dynamically, thereby improving the accuracy of the remaining life prediction of the equipment under the actual situation of imperfect maintenance. Spend.

Description

technical field [0001] The invention relates to a technology for predicting the remaining life of high-reliability and long-life equipment, in particular to a method for predicting imperfect maintenance of the remaining life of equipment based on indirect monitoring data. Background technique [0002] With the rapid development of my country's science and technology level and the rapid improvement of the industrial level, there are a large number of high-reliability and long-life equipment in some fields that require high reliability and safety. However, this kind of equipment has the characteristics of high production cost, small production quantity and complex failure mechanism. How to predict its remaining useful life (Remaining Useful Life, RUL) has become a hot issue in the field of reliability. Traditional reliability analysis methods take the failure time of equipment as the research object, which will not only bring high economic costs, but also cause unnecessary was...

AI Technical Summary

Problems solved by technology

However, this type of equipment has the characteristics of high production cost, small production quantity, and complex failure mechanism. How to predict its remaining useful life (Remaining Useful Life, RUL) has become a hot issue in the field of reliability.

Traditional reliability analysis methods take the failure time of equipment as the research object, which will not only bring high economic costs, but also cause unnecessary waste of time

In addition, traditional methods only use failure time for analysis, and a large amount of useful information in the process of equipment degradation is ignored

Since the degradation failure of high-cost and long-life equipment is closely related to its performance degradation, the accuracy of the final prediction result cannot be guaranteed, and the prediction accuracy is low

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