Acoustic Emission Method for Locating and Identifying Active Destructive Processes in Operating Facilities

Abstract

Featured Application: The data collected can be the basis for determining the structural condition of the structure.Abstract: Durability, safety, and usability are the three foundations of structural reliability, vital in the economic and social context. As the locating and tracking of potential damage and evaluating its impact on the condition of the structure are part of service life assessment, relevant methods should be developed that would detect the onset of the deterioration process and enable the monitoring of its progress within the entire volume of the structure, not only in the areas selected in a subjective way. The acoustic emission (AE) method relying on the analysis of active destructive processes can be the best choice. This article reports the results of the application of the AE method for identifying active destructive processes and tracking their development during the routine operation of various types of structures.

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

“…They successfully evaluated oil refinery tanks and towers, pipelines, rotary kilns, bridge structures, drag lines, etc. Swit [11] also covered AE detection of active destructive processes that are in progress on various structures, including steel bridges, steel columns, and a large suspension bridge. The use of pattern recognition analysis was effective in identifying active damage progression.…”

Localization and identification of gas infrastructure defects by acoustic emission

“…To build a base of reference signals in the method of identifying destructive processes in gas infrastructure (IDPGI) [6][7] the NOESIS 5.8 program is used, which uses hierarchical, non-hierarchical statistical clustering methods and neural networks.…”

“…Two types of fuzzy clustering can be distinguished: the first uses fuzzy relations to clustering; the second type is based on the criterion function, on the basis of which fuzzy groups are formed. A simplified scheme of the FORGY algorithm using fuzzy clustering consists in the following steps: 1. determining the input parameters of the algorithm like the number of intervals c, the ending criterion E, blur factor, membership coefficients U (0), cycle index t = 1, 2. calculation of the vector formula V(t) and array U(t) according to formula (7) and (8)…”

“…The method is based on a comparative analysis of acoustic emission signals registered during tests and collected in the database of reference signals corresponding to individual destructive processes. The results are used to identify and locate active destructive processes in gas infrastructure and in steel constructions, thus enabling global monitoring in the entire monitored area and recording only active developing lesions in real burden conditions [7][8][9].…”

Using AE Signals to Investigate the Fracture Process in an Al–Ti Laminate

“…That is why using AE in the study made it possible to identify certain peculiarities of the fracture process in the tested layered composite. Also, the AE signals were clustered depending on the processes that generated them using the iterative k -means method, which clusters AE parameters in a Euclidean space [ 30 , 31 ], and analysed them using waveform time domain, waveform time domain (autocorrelation), fast Fourier transform (FFT Real) and waveform continuous wavelet based on the Morlet wavelet [ 32 ].…”