Equipment residual life imperfect maintenance prediction method based on indirect monitoring data

Abstract

The invention discloses an equipment residual life imperfect maintenance prediction method based on indirect monitoring data, and the method comprises the following steps: analyzing the physical structure of equipment, and recognizing a key parameter which can describe the degradation process of the equipment; collecting key parameter degradation data obtained by monitoring; estimating parametersof the degradation model by using the collected data; establishing an equipment residual life imperfect maintenance prediction model based on indirect monitoring data according to the estimated parameter values; and predicting the residual life of the equipment by utilizing the collected degradation data according to the established prediction model. Compared with a traditional method, the economic cost of the test is reduced, the research period is shortened, and the influence of the environment on the equipment can be reflected in a richer and more dynamic manner, so that the accuracy of predicting the residual life of the equipment under the condition of considering imperfect maintenance practice is improved.

Description

technical field [0001] The invention relates to a high-reliability and long-life equipment remaining life prediction technology, 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 waste of time....

AI Technical Summary

Problems solved by technology

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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