Acoustic Emission Signal Characteristic Analysis And Risk Assessment Of Standard Section Crack Of Tower Crane

The standard section is the main sustaining structure of tower crane,its safety is important to the normal running.Tower crane often works in outfield,the complicated loading condition weakens the strength of standard section.In recent years,the failure of standard section have made a huge loss.We have to put an eye on the safety of standard section of tower crane.Therefore,it is meaningful to study the AE character of the crack in standard section and the assessment of risk of standard section.AE tech is a kind of non-destructive testing method used in pressure vessel detection.Compared with others non-destructive testing tech,AE can obtain the structure’s signal character in loading process.As a result,it is suitable to use the AE tech to detect the tower crane standard section.In hoisting equipment AE detecting area,Chinese national standard committee has published GB/T 32544-2016 Acoustic emission examination and evaluation methods of steel structures of bridge and gantry cranes.However,we still have to research a lot about risk assessment of tower crane standard section.In this paper,I have done the following research:(1)After referring to the former AE study,I designed several kinds of specimens made of Q235.I performed the stretching experiment,and collected the AE signal through the stretching process.(2)To analyze the AE signal collected in the experiment,and made the correlation diagram and the sequence chart with AE parameter,compared the difference of them in three phase such as,plastic phase,elastic phase and necking phase.Finally,to distinguish the specimens with correlation diagram and the sequence chart.(3)In order to analyse the correlation graph,I made a calculation program with Matlab to calculate the fractal dimension of Q235 specimens.After getting the results of fractal dimension of different specimens,I summarized the variation trend of them,set the relationship between fractal dimension and the stretching stage,and made the contrast of the different specimens.(4)In order to estimate crack risk of the standard sections,I used BP neural network tech to analyse the defects;Adding the overloading factor in the modified hoisting machine model to optimize the risk model.Using the results of BP neural network and fractal number to value the damage factor,finally to reduce the human judgement.