Pre-failure indicators detected by Acoustic Emission: Alfas stone, cement-mortar and cement-paste specimens under 3-point bending

2018 ◽  
Vol 11 (40) ◽  
pp. 74-84
Author(s):  
Stavros K. Kourkoulis ◽  
Ioanna Dakanali

Acoustic Emission (AE) is the technique most widely used nowadays for Structural Health Monitoring (SHM). Application of this technique for continuous SHM of restored elements of stone monuments is a challenging task. The co-existence of different materials creates interfaces rendering “identification” of the signals recorded very complicated. To overcome this difficulty one should have a clear overview of the nature of AE signals recorded when each one of the constituent materials is loaded mechanically. In this direction, an attempt is here described to enlighten the signals recorded, in case a series of structural materials (natural and artificial), extensively used for restoration projects of classic monuments in Greece, are subjected to 3-point bending. It is hoped that obtaining a clear understanding of the nature of AE signals recorded during these elementary tests will provide a valuable tool permitting “identification” and “classification” of signals emitted in case of structural tests. The results appear encouraging. In addition, it is concluded that for all materials tested (in spite their differences in microstructure and composition) clear prefailure indicators are detected, in good accordance to similar indicators provided by other techniques like the Pressure Stimulated Currents (PSC) one.

Materials ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 897
Author(s):  
Sagar Jinachandran ◽  
Ginu Rajan

Fiber Bragg grating (FBG)-based acoustic emission (AE) detection and monitoring is considered as a potential and emerging technology for structural health monitoring (SHM) applications. In this paper, an overview of the FBG-based AE monitoring system is presented, and various technologies and methods used for FBG AE interrogation systems are reviewed and discussed. Various commercial FBG AE sensing systems, SHM applications of FBG AE monitoring, and market potential and recent trends are also discussed.


2014 ◽  
Vol 891-892 ◽  
pp. 1255-1260 ◽  
Author(s):  
Sanghyun Yoo ◽  
Akbar Afaghi Khatibi ◽  
Everson Kandare

Structural Health Monitoring (SHM) systems are developed to decrease the maintenance cost and increase the life of engineering structures by fundamentally changing the way structural inspections are performed. However, this important objective can only be achieved through the consistent and predictable performance of a SHM system under different service conditions. The capability of a Piezoelectric lead Zirconate Titanate (PZT)-based SHM system in detecting structural flaws strongly depends on the sensor signals as well as actuator performance. But service conditions can change the behaviour of transducers, raising questions about long term SHM system capability. Although having a clear understanding of the reliable sensor life is important for surface mounted systems, however, this is particularly critical for embedded sensors. This is due to the fact that opportunity for replacement of sensors exists for surface bonded transducers while for the embedded systems, sensor replacement is not straightforward. Therefore, knowledge of the long term behaviour of embedded-SHM systems is critical for their implementation. This paper reports a study on the degradation of embedded PZT transducers under cyclic loadings. Carbon/epoxy laminates with an embedded PZT were subjected to fatigue loading and their performance was monitored using Scanning Laser Vibrometery (SLV). The functionality of PZT transducers under sensing and actuating modes were studied. High and low cycle fatigue tests were performed to establish strain-voltage relationships which can be used to identify critical cyclic loading parameters (number of cycles and R value) under sensing and actuating modes.


2020 ◽  
Vol 19 (6) ◽  
pp. 2007-2022
Author(s):  
John P McCrory ◽  
Matthew R Pearson ◽  
Rhys Pullin ◽  
Karen M Holford

Structural health monitoring has gained wide appeal for applications with high inspection costs, such as aircraft and wind turbines. As the structures and materials used in these industries evolve, so too must the technologies used to monitor them. Acoustic emission is a passive method of detecting damage which lends itself well to structural health monitoring. One form of acoustic emission monitoring, known as wavestreaming, involves intermittently recording data for set periods of time and using the sequential recordings to detect changes in the state of the structure. However, at present, there is no standard method for selecting appropriate wavestream recording parameters, such as their length or their interval of collection. This article investigates a method of optimising acoustic emission wavestreaming for structural health monitoring purposes by introducing the novel concept of adjoining consecutive discrete acoustic emission hit signals to create synthetic wavestreams. To this end, a pre-notched 492 mm × 67.5 mm × 20 mm, 300M grade steel cantilever specimen was subject to cyclic loading and both acoustic emission hit data and conventional wavestreams were collected as a crack grew in the notched region; crack growth activity was also monitored using digital image correlation for comparison. To demonstrate the proposed optimisation process, four sets of synthetic wavestreams were created from the hit data, 0.25, 0.5, 1.0 and 1.5 s in length, and compared with the 1.5-s-long conventional wavestreams. The activity of the peak frequency and frequency centroid bands of interest within the conventional and synthetic wavestreams were examined to determine whether or not cracking activity could be inferred through them. Across comparisons of all data, it was found that the 0.5-s-long synthetic wavestreams contained enough information to identify the same trends as the conventional wavestreams for this application; thus, the use of synthetic wavestreams as a tool for selecting an appropriate wavestream recording length was demonstrated.


Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1716
Author(s):  
David Agis ◽  
Francesc Pozo

In this paper, we evaluate the performance of the so-called parametric t-distributed stochastic neighbor embedding (P-t-SNE), comparing it to the performance of the t-SNE, the non-parametric version. The methodology used in this study is introduced for the detection and classification of structural changes in the field of structural health monitoring. This method is based on the combination of principal component analysis (PCA) and P-t-SNE, and it is applied to an experimental case study of an aluminum plate with four piezoelectric transducers. The basic steps of the detection and classification process are: (i) the raw data are scaled using mean-centered group scaling and then PCA is applied to reduce its dimensionality; (ii) P-t-SNE is applied to represent the scaled and reduced data as 2-dimensional points, defining a cluster for each structural state; and (iii) the current structure to be diagnosed is associated with a cluster employing two strategies: (a) majority voting; and (b) the sum of the inverse distances. The results in the frequency domain manifest the strong performance of P-t-SNE, which is comparable to the performance of t-SNE but outperforms t-SNE in terms of computational cost and runtime. When the method is based on P-t-SNE, the overall accuracy fluctuates between 99.5% and 99.75%.


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