Vibration and Acoustic Emission Monitoring of a Girth Weld during a Resonance Fatigue Test

This research is focused on LEVEL 1 detection and LEVEL 2 localization of damage which could be used as part of a structural health monitoring strategy. An experimental trial of acoustic emission and vibration monitoring techniques was carried out to monitor fatigue damage during a full scale resonance fatigue test on a girth welded pipe. The welded steel pipe was excited into the first mode of vibration using the resonance fatigue testing technique in order to determine the high cycle fatigue strength of the weld. The test applies a cyclic bending stress around the full pipe circumference at around 30Hz. The test was monitored in real time using acoustic emission and vibration monitoring. The damage sensitive features of the output from the vibra-tion data were compared with the acoustic emission parameters. This paper shows that combin-ing the vibration monitoring and acoustic emission results was effective since both techniques could detect damage from fatigue cracking in the extreme test conditions

Bayesian Estimation for Crack Monitoring of a Resonant Pipe using Acoustic Emission Method

Vibration induced fatigue is a well-known problem in the oil and gas piping and pipeline systems. However the use of vibration data to detect damage is not an easy task without a priori knowledge about the undamaged condition. In this paper, acoustic emission monitoring of a resonance fatigue test of a welded carbon steel pipe has been carried out to monitor the weld condition from healthy until failure. The information provided by acoustic emission monitoring is useful in evaluating the condition of the pipe during the test and the occurrence of cracking before failure. However, acoustic emission signals are also susceptible to extraneous noises. Therefore in this work, the acoustic emission signals from different combination of sensors were recursively correlated, which provides parameter for Bayesian estimation to discriminate the true signals and avoid false detection and localization errors. The method provides indicator for damage detection, which shows a strong relationship of the acoustic emission energy and the estimated coefficients. High correlation of signals was found to be associated with cracking, which has an effect of the increase of acoustic emission energy. Likewise low correlation of signals was found to be associated with random signals or noises. The method will be useful in monitoring piping or pipelines condition to reduce the risk of vibration induced fatigue failure

Application of Bayesian Estimation to Structural Health Monitoring of Fatigue Cracks in Welded Steel Pipe

Vibration induced fatigue is a well-known problem in oil and gas piping systems [1]. Various vibration-based monitoring techniques have been implemented in the field of condition (CM) and structural health monitoring (SHM). However, the major challenge in monitoring technology is to detect failure with high confidence. In principle, vibration-based techniques evaluate structural condition from physical and dynamic characteristics. This method is useful particularly for damage detection and localization. However, the information obtained may be insufficient for complex problems of detection and localization of cracks, where the scale of damage is relatively small compared to the overall size of piping system. The difficulty arises due to negligible changes in structural stiffness, and thus no observable change in natural frequencies. Unmeasurable local stresses at crack tips further increase the risk of fatigue failure. In this paper, acoustic emission (AE) is used for damage detection and localization in a welded pipe subjected to dynamic loading by resonance fatigue testing. Acoustic emission is a passive non-destructive testing (NDT) technique due to rapid changes of stress state such as during crack extension. The energy is released as elastic waves that travel within material in the form of microscopic displacements and are then converted into electrical signals by AE sensors. The acoustic emission method is very sensitive to crack evolution. However, the signals are also susceptible to extraneous noise. Therefore Bayesian estimation, which uses a probabilistic approach to estimate the unknown parameters for damage evaluation, is proposed. The estimation was carried out using a signal-based method where the parameter used for Bayesian estimation is derived directly from the signals.Fatigue Integrated Management and Condition & Structural Monitoring sections at TWI, Brunel University Londo