The effect of the type of AE sensor and mounting methods on the location accuracy of artificial AE sources on the bottom plate of liquid (water and grease) storage cylindrical tanks of 820mm in diameter was studied. Location accuracies using the direct P-waves through liquids and direct Lamb waves through the bottom plate were compared. Source locations of lead-breaking and emery paper scouring on the bottom plate were estimated utilizing the P-waves monitored by three types of AE sensors (resonant type PICO of 450kHz, R6I of 60kHz and R3I of 30kHz) mounted on the tank wall. Arrival times of the direct P-waves through liquids were determined using wavelet transform at selected frequencies and were submitted to a virtual source scanning location method. Location accuracy was better when the arrival times of a 90kHz component of AE signals by the R6I sensor were utilized. Location accuracy was worst when R3I sensors were used. Attenuations of the Lamb waves through the bottom plate loaded with liquids were smaller than those of the P-waves through liquids and were successfully utilized for the source location. Location accuracy utilizing the 70kHz component of an Ao-Lamb wave detected by R6I sensors mounted on the dog-running space of the bottom plate was much higher than those utilizing the P-wave through liquids.

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We monitored AEs from a self-fracture of atmospheric rust produced on circular steel plates (SS400) welded in a SUS304 bottom plate of a 820mm diameter water-storage cylindrical tank. The tank was set on the tar-sand base and exposed to outdoor weathering for 61 days. AEs were monitored as Lamb waves using four resonant-type AE sensors (PAC, type-R6I with center frequency of 60kHz) mounted on the dog-running surface of the bottom plate. AE monitoring at 20dB amplification for one day recorded AE events with large amplitude and higher S/N ratio. AE event counts increased after 15 days exposure and were correctly located to the circular steel plates which suffered higher wall reduction by atmospheric corrosion. Next we monitored AEs from a tank exposed for 80 days at 60dB amplification for one hour using the R6I sensors mounted on the dog-running surface of the bottom plate and shell wall. AE events monitored by the bottom-plate sensors were located correctly in the heavily corroded carbon steel plates, while location accuracy of AE events by the shell-wall sensors was poor.

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In a previous we proposed a new non-contacting non-destructive method for detecting cracks in concrete structures utilizing an impact by shock waves and vibration detection by LASER vibrometers. It has been found that a shock wave excites flexural vibration in the concrete plate above shallow cracks and results in an exponentially decaying sinusoid in the vibration signal. In the present article a signal processing procedure for testing the concrete integrity is proposed. The method automatically detects an exponentially decaying sinusoid that is, a flexural vibration of the concrete surface. The velocity signal is first segmented into successive frames. In each frame, the signal is analyzed using the linear prediction model (AR model) and the prediction error normalized by the signal power is calculated. This error indicates the degree to which the signal is modeled in the form of an exponentially decaying sinusoid, and presence of sub-surface flaws in the concrete can be determined from its value. When a flexural vibration (a defect in concrete) is detected, its frequency is estimated by solving a modified Yule-Walker equation or using FFT. The proposed procedure was applied to computer-generated signals and actual vibration signals, and the success of the procedure was verified.

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