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Acoustic emission sources localization and identification of complex metallic structures based on nearfield frequency space sparse decomposition
Currently, structural health monitoring (SHM) of complex metallic structures based on the localization and identification of acoustic emission (AE) sources has become one of the most common condition monitoring method. However, existing methods are difficulty in accurately localizing and identifying AE sources generated by complex metallic structures that have been surface modified or machined. To overcome this problem, this paper presents a novel architecture named nearfield frequency space sparse decomposition (NFSSD) for localizing AE sources collected from complex metallic structures. Main contributions of the proposed NFSSD are to incorporate the decomposed subbands of AE signal in frequency into the traditional sparse decomposition (SD), which can extract more effective information and improve the identification of coherent AE sources. On this basis, NFSSD‐based AE feature extraction scheme is further proposed for improving the accuracy and stability of AE source localization for complex metallic structures. First, all frequency point estimates of the original AE signal used to divide the subbands are obtained, where each frequency corresponds to the center frequency of the subband. Furthermore, the spatial spectrum of each subband signal is solved over the entire spatial domain, and the spatial spectrum of the signal is obtained to estimate the location of AE source. Two experimental results of coordinate‐based AE source localization of complex metallic structures indicate that the proposed method has better AE source localization performance compared to conventional localization approaches. Specifically, the results show that the proposed approach can provide an effective theoretical reference for AE‐based SHM of complex metallic structures.
Acoustic emission sources localization and identification of complex metallic structures based on nearfield frequency space sparse decomposition
Currently, structural health monitoring (SHM) of complex metallic structures based on the localization and identification of acoustic emission (AE) sources has become one of the most common condition monitoring method. However, existing methods are difficulty in accurately localizing and identifying AE sources generated by complex metallic structures that have been surface modified or machined. To overcome this problem, this paper presents a novel architecture named nearfield frequency space sparse decomposition (NFSSD) for localizing AE sources collected from complex metallic structures. Main contributions of the proposed NFSSD are to incorporate the decomposed subbands of AE signal in frequency into the traditional sparse decomposition (SD), which can extract more effective information and improve the identification of coherent AE sources. On this basis, NFSSD‐based AE feature extraction scheme is further proposed for improving the accuracy and stability of AE source localization for complex metallic structures. First, all frequency point estimates of the original AE signal used to divide the subbands are obtained, where each frequency corresponds to the center frequency of the subband. Furthermore, the spatial spectrum of each subband signal is solved over the entire spatial domain, and the spatial spectrum of the signal is obtained to estimate the location of AE source. Two experimental results of coordinate‐based AE source localization of complex metallic structures indicate that the proposed method has better AE source localization performance compared to conventional localization approaches. Specifically, the results show that the proposed approach can provide an effective theoretical reference for AE‐based SHM of complex metallic structures.
Acoustic emission sources localization and identification of complex metallic structures based on nearfield frequency space sparse decomposition
Li, Yang (author) / Lee, Chi‐Guhn (author) / Xu, Feiyun (author)
2022-11-01
21 pages
Article (Journal)
Electronic Resource
English
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