Acoustic emission source location from P-wave arrival time corrected data and virtual field optimization method. (15th January 2022)

• Record Type: Journal Article
• Authors: Shang, Xueyi; Wang, Yi; Miao, Runxue
• Is Part Of: Mechanical systems and signal processing. Volume 163(2022)
• Publication Date: 2022-01-15
• Subjects: Acoustic emission (AE) -- Source location -- P-wave arrival time system error -- Bayesian inversion -- Virtual field optimization method (VFOM); Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.108129
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: P-wave arrival time system error (PATSE) was observed during pencil-lead break tests. A Bayesian framework using MCMC sampling was established to estimate the PATSE. The improved VFOM has a better location result than the L1 and L2 norm based methods. The PATSE corrected data based method has a good location accuracy. Abstract: Acoustic emission (AE) event location plays an important role in structural safety assessments. However, accurately locating an AE event is usually difficult, especially for a small structure. Interestingly, a pencil-lead break (PLB) experiment shows that the P-wave arrival time of a sensor is always significantly different (~260 µs) from that of the other sensors. This time difference is much greater than the P-wave travel time in the range of the experimental sample. Therefore, it can be inferred that there is a P-wave arrival time system error (PATSE) for each sensor. The PATSE may be due to a combined result of the sensor site effect and signal transfer delay time from the sensor to the signal storage. To handle this, a Bayesian inversion framework was built to estimate PATSEs. A synthetic test demonstrated the effectiveness of the proposed Bayesian method for noisy P-wave arrival time data. Then, Bayesian inversion was applied to 15 PLB events, which confirmed the existence of PATSE in an AE experiment for the first time. The average PATSE reached 1.47 µs without considering the P-wave arrival time significantly different sensor. The average location error of 25 PLB events was 14.30 mm and 6.58 mm for PATSE unremoved and removed data, respectively. To achieve this, a high-precision virtual field optimization location method (VFOM) was used. This demonstrates the necessity of removing the PATSEs. Finally, the AE event location performance for the PATSE unremoved and removed data was compared, where the AE events were obtained from the uniaxial compression of a red sandstone sample. The results indicated that there was a higher location detection success rate for the corrected data. The AE locations based on the corrected data were in a better correlation with the rock sample failure mode than that without correction. Moreover, increase the signal sampling frequency for AE event identification, use a real-time inverted 3D velocity model and update the PATSEs in real time could be used to further improve the AE event location accuracy.