3D reconstruction of cracks propagation in mechanical workpieces analyzing non-stationary acoustic mixtures. (15th March 2019)

• Record Type: Journal Article
• Authors: Shevchik, Sergey A.; Meylan, Bastian; Violakis, Georgios; Wasmer, Kilian
• Is Part Of: Mechanical systems and signal processing. Volume 119(2019)
• Page Start: 55
• Page End: 64
• Publication Date: 2019-03-15
• Subjects: Acoustic emission -- Source localization -- Cracks -- Non-stationary mixtures -- Independent component analysis; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2018.09.022
• Languages: English
• ISSNs: 0888-3270

Abstract

Graphical abstract: Highlights: A 3D crack reconstruction method is proposed. The algorithm was tested using synthetic data and confirmed by real cracking data. Our approach combines acoustic emission with independent component analysis. Fiber Bragg grating is used as acoustic sensors. Abstract: Cracks are one of the main causes of solid bodies weakening and are a hidden threat for mechanical systems. Acoustic emission is an indicator of cracks initiation and propagation and is widely exploited for the localization of cracks in non- or weakly-transparent environments. The present study enriches the functionality of multiple existing algorithms in crack localization using acoustics. The novelty is in the reconstruction of the complex geometry of the crack path and tracking its propagation in time, whereas existing methods focus only on the localization of the crack without any information about its geometry. The algorithm uses sparse acoustic signal representations as relative energies of the narrow frequency bands, extracted with the M- band wavelet transform. Non-linear independent component analysis is applied to de-mix the recorded acoustic signals into a number of separate acoustic patterns. Furthermore, a triangulation, using the time delay arrivals, is applied for each of such patterns separately, thus extracting multiple individual emittance sources. The algorithm was tested using synthetic data that replayed various scenarios of crack propagation together with different detector arrays configurations and its behavior was analyzed. Additional verification with real-time data was carried out by analyzing signals from crack propagation in glass along a known programmed path. The acoustic data was recorded with four fiber Bragg gratings. In both cases, the algorithmic framework showed a high efficiency in recovering the geometrical configuration of the crack.