The setting of Acoustic Emission detecting parameters in running of steel structured
Acoustic Emission technology is still at the starting stage of monitoring in domestic bridge. To resolve the problem that Acoustic Emission detection parameters in scene are determined. Based on lead-breaking tests confirm threshold and time parameter. Studying on the characteristic of signal propagated and the attenuation laws of velocity and amplitude, it gets references for Acoustic emission technique in detection of the bridge.
Monitoring Damage of Arch Bridge Suspender Using Acoustic Emission Technique
Conventional non-destructive methods can't be dynamically monitored the suspenders'damage levels and types,so acoustic emission(AE) technique is proposed to monitor its activity.The validity signals are determined by the relationship with AE rise time and duration time.The ambient noise is eliminated using float threshold value and placing a guard sensor.The cement mortar and steel strand damage level is analyzed by AE parameter method and damage types are judged by waveform analyzing technique.Based on these methods,all the suspenders of Sichuan Ebian Dadu river arch bridge have been monitored using AE techniques.The monitoring results show that AE signal amplitude,energy and counts can visually display the suspenders'damage levels,the difference of waveform and frequency range express different damage type.The testing results are well coincide with the practical situation.
Distribution-free monitoring of univariate processes
We consider statistical process control (SPC) of univariate processes when observed data are not normally distributed. Most existing SPC procedures are based on the normality assumption. In the literature, it has been demonstrated that their performance is unreliable in cases when they are used for monitoring non-normal processes. To overcome this limitation, we propose two SPC control charts for applications when the process data are not normal, and compare them with the traditional CUSUM chart and two recent distribution-free control charts. Some empirical guidelines are provided for practitioners to choose a proper control chart for a specific application with non-normal data.