Crack information prediction based crane operation state online evaluation method

Abstract

The invention discloses a crack information prediction based crane operation state online evaluation method. The method includes the following steps: S1, actual measured stress data based fixed time node crack expansion prediction; S2, known time node data based real-time crack expansion information prediction; S3, fixed time node crack expansion data update; and S4, crack expansion information based crane real-time operation state evaluation. The evaluation method takes crack expansion information as an evaluation index, considers all the factors influencing the operation state of the crane, can effectively avoid the defect that the consideration factors are few, and real-time update is slow in the prior art, can greatly improve the precision of an evaluation result and effectively improve the efficiency of real-time evaluation, and can achieve accurate and effective crane operation state online evaluation.

Description

technical field [0001] The invention relates to a method for evaluating the operating state of a crane, in particular to an online evaluation method for the operating state of a crane based on crack information prediction. Background technique [0002] With the progress of society, crane equipment is developing in the direction of large-scale. Under such a background, small damages will cause huge losses, so it is necessary and urgent to predict and evaluate the operating status of crane equipment to grasp its real-time damage status. There are many evaluation theories of operating status, but the results predicted purely based on these theories have poor accuracy and slow real-time update. The main reason is that many external factors will affect the prediction results and the data prediction update of common methods is relatively slow. slow. Therefore, the low prediction accuracy and the inability to realize the rapid update of forecast data are common problems in this r...

AI Technical Summary

Problems solved by technology

There are many evaluation theories of operating status, but the results predicted purely based on these theories have poor accuracy and slow real-time update. The main reason is that many external factors will affect the prediction results and the data prediction update of common methods is relatively slow. slow

Therefore, the low prediction accuracy and the inability to quickly update the prediction data are common problems in this research field.

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