FATIGUE CRACK MONITORING WITH COUPLED PIEZOELECTRIC FILM ACOUSTIC EMISSION SENSORS

Fatigue-induced cracking is a commonly seen problem in civil infrastructures reaching their original design life. A number of high-profile accidents have been reported in the past that involved fatigue damage in structures. Such incidences often happen without prior warnings due to lack of proper crack monitoring technique. In order to detect and monitor the fatigue crack, acoustic emission (AE) technique, has been receiving growing interests recently. AE can provide continuous and real-time monitoring data on damage progression in structures. Piezoelectric film AE sensor measures stress-wave induced strain in ultrasonic frequency range and its feasibility for AE signal monitoring has been demonstrated recently. However, extensive work in AE monitoring system development based on piezoelectric film AE sensor and sensor characterization on full-scale structures with fatigue cracks, have not been done. A lack of theoretical formulations for understanding the AE signals also hinders the use of piezoelectric film AE sensors. Additionally, crack detection and source localization with AE signals is a very important area yet to be explored for this new type of AE sensor. This dissertation presents the results of both analytical and experimental study on the signal characteristics of surface stress-wave induced AE strain signals measured by piezoelectric film AE sensors in near-field and an AE source localization method based on sensor couple theory. Based on moment tensor theory, generalized expression for AE strain signal is formulated. A special case involving the response of piezoelectric film AE sensor to surface load is also studied, which could potentially be used for sensor calibration of this type of sensor. A new concept of sensor couple theory based AE source localization technique is proposed and validated with both simulated and experimental data from fatigue test and field monitoring. Two series of fatigue tests were conducted to perform fatigue crack monitoring on large-scale steel test specimens using piezoelectric film AE sensors. Continuous monitoring of fatigue crack growth in steel structures is demonstrated in these fatigue test specimens. The use of piezoelectric film AE sensor for field monitoring of existing fatigue crack is also demonstrated in a real steel I-girder bridge located in Maryland. The sensor couple theory based AE source localization is validated using a limited number of piezoelectric film AE sensor data from both fatigue test specimens and field monitoring bridge. Through both laboratory fatigue test and field monitoring of steel structures with active fatigue cracks, the signal characteristics of piezoelectric film AE sensor have been studied in real-world environment

Improved acoustic emission source location during fatigue and impact events in metallic and composite structures

In order to overcome the difficulties in applying traditional Time-Of Arrival (TOA) techniques for locating Acoustic Emission (AE) events in complex structures and materials, a technique termed “delta-t mapping” was developed. This paper presents a significant improvement on this, in which the difficulties in identifying the precise arrival time of an AE signal are addressed by incorporating the Akaike Information Criteria (AIC). The performance of the TOA, the delta-t mapping and the AIC delta-t mapping techniques is assessed by locating artificial AE sources, fatigue damage and impact events in aluminium and composite materials respectively. For all investigations conducted the improved AIC delta-t technique shows a reduction in average Euclidean source location error irrespective of material or source type. For locating H-N sources on a complex aluminium specimen the average source location error (Euclidean) is 32.6, (TOA), 5.8 (delta-t) and 3mm (AIC delta-t). For locating fatigue damage on the same specimen the average error is 20.2, (TOA), 4.2 (delta-t) and 3.4mm (AIC delta-t). For locating H-N sources on a composite panel the average error is 19.3, (TOA), 18.9 (delta-t) and 4.2mm (AIC delta-t). Finally the AIC delta-t mapping technique had the lowest average error (3.3mm) when locating impact events when compared with the delta-t (18.9mm) and TOA (124.7mm) techniques. Overall the AIC delta-t mapping technique is the only technique which demonstrates consistently the lowest average source location error (greatest average error 4.2mm) when compared with the delta-t (greatest average error 18.9mm) and TOA (greatest average error 124.7mm) techniques. These results demonstrate that the AIC delta-t mapping technique is a viable option for AE source location, increasing the accuracy and likelihood of damage detection, irrespective of material, geometry and source type

Crack monitoring in historical masonry with distributed strain and acoustic emission sensing techniques

The analysis of crack patterns and crack growth is one of the most important steps in the assessment of structural damage in historical masonry. In a search for integrated and accurate monitoring techniques for crack measurements in masonry, several novel techniques based on distributed strain monitoring and acoustic emission (AE) sensing have been investigated in an experimental test campaign. Aim of the test program was to develop integration procedures for the strain and AE sensors, analyse their use for crack monitoring specifically in historical masonry and assess their robustness and efficiency with respect to the experimentally observed crack pattern.This work is performed within the framework of the GEPATAR project (“GEotechnical and Patrimonial Archives Toolbox for ARchitectural conservation in Belgium” BR/132/A6/GEPATAR), which is financially supported by BRAIN-be, Belspo.Postprint (updated version

Structural Health Monitoring of Large Structures Using Acoustic Emission-Case Histories

Acoustic emission (AE) techniques have successfully been used for assuring the structural integrity of large rocket motorcases since 1963 [...

On the evolution of elastic properties during laboratory stick-slip experiments spanning the transition from slow slip to dynamic rupture

The physical mechanisms governing slow earthquakes remain unknown, as does the relationship between slow and regular earthquakes. To investigate the mechanism(s) of slow earthquakes and related quasi-dynamic modes of fault slip we performed laboratory experiments on simulated fault gouge in the double direct shear configuration. We reproduced the full spectrum of slip behavior, from slow to fast stick slip, by altering the elastic stiffness of the loading apparatus (k) to match the critical rheologic stiffness of fault gouge (kc). Our experiments show an evolution from stable sliding, when k>kc, to quasi-dynamic transients when k ~ kc, to dynamic instabilities when k<kc. To evaluate the microphysical processes of fault weakening we monitored variations of elastic properties. We find systematic changes in P wave velocity (Vp) for laboratory seismic cycles. During the coseismic stress drop, seismic velocity drops abruptly, consistent with observations on natural faults. In the preparatory phase preceding failure, we find that accelerated fault creep causes a Vp reduction for the complete spectrum of slip behaviors. Our results suggest that the mechanics of slow and fast ruptures share key features and that they can occur on same faults, depending on frictional properties. In agreement with seismic surveys on tectonic faults our data show that their state of stress can be monitored by Vp changes during the seismic cycle. The observed reduction in Vp during the earthquake preparatory phase suggests that if similar mechanisms are confirmed in nature high-resolution monitoring of fault zone properties may be a promising avenue for reliable detection of earthquake precursors

Structural Health Monitoring of Pipelines in Radioactive Environments Through Acoustic Sensing and Machine Learning

Structural health monitoring (SHM) comprises multiple methodologies for the detection and characterization of stress, damage, and aberrations in engineering structures and equipment. Although, standard commercial engineering operations may freely adopt new technology into everyday operations, the nuclear industry is slowed down by tight governmental regulations and extremely harsh environments. This work aims to investigate and evaluate different sensor systems for real-time structural health monitoring of piping systems and develop a novel machine learning model to detect anomalies from the sensor data. The novelty of the current work lies in the development of an LSTM-autoencoder neural network to automate anomaly detection on pipelines based on a fiber optic acoustic transducer sensor system. Results show that pipeline events and faults can be detected by the MLM developed, with a high degree of accuracy and low rate of false positives even in a noisy environment near pumps and machinery