Data association relationship-based equipment defect assessment and prediction method

Abstract

The invention discloses a data association relationship-based equipment defect assessment and prediction method. The method mainly comprises the following steps: integrating historical monitoring data of power grid equipment, and extracting a certain equipment defect type of the power grid equipment and data factors corresponding to the equipment defect type; integrating the data factors corresponding to the equipment defect type so as to obtain a factor monitoring database, wherein the factor monitoring database comprises monitoring data of the equipment defect type; obtaining association coefficients of elements in a set of the data factors corresponding to the equipment defect type; constructing an assessment and prediction model of the equipment defect type; and combining the assessment and prediction model and the factor monitoring database, calculating all assessment and prediction model y values corresponding to the defect type, and carrying out equipment defect assessment and prediction according to the assessment and prediction model of the equipment defect type and an interval distribution probability corresponding to all the equipment defect types. According to the method, the defect analysis flexibility is enhanced.

Description

technical field [0001] The invention relates to the technical field of electric equipment management, and more specifically, relates to a method for evaluating and predicting equipment defects based on data association relationships. Background technique [0002] With the continuous improvement of the demand for digital management of power grid systems and the continuous integration of various business production systems in recent years, the accumulated storage of power business data has grown rapidly. These data have a large amount of information and contain important information, and are data references for equipment management. Such analysis and application of these huge, complex and objective online monitoring, test, maintenance data and hidden information between the data is of great significance for improving the risk management and prevention level of power grids and power equipment. However, due to the various types of this data, it is difficult to view and apply it...

AI Technical Summary

Problems solved by technology

However, using this method to consider all factors of equipment may increase the computational complexity of the model and reduce the applicability and accuracy of the model

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