The development of new low-cost transducers and systems has been extensively aimed at in both industry and academia to promote a correct failure diagnosis in aerospace, naval, and civil structures. In this context, structural health monitoring (SHM) engineering is focused on promoting human safety and a reduction in the maintenance costs of these components. Traditionally, SHM aims to detect structural damages at the initial stage, before it reaches a critical level of severity. Numerous approaches for damage identification and location have been proposed in the literature. One of the most common damage location techniques is based on acoustic waves triangulation, which stands out as an effective approach. This method uses a piezoelectric transducer as a sensor to capture acoustic waves emitted by cracks or other damage. Basically, the damage location is defined by calculating the difference in the time of arrival (TOA) of the signals. Although it may be simple, the detection of TOA requires complex statistical and signal processing techniques. Based on this issue, this work proposes the evaluation of a low-cost piezoelectric transducer to determine damage location in metallic structures by comparing two methodologies of TOA identification, the Hinkley criterion and the statistical Akaike criterion. The tests were conducted on an aluminum beam in which two piezoelectric transducers were attached at each end. The damage was simulated by pencil lead break (PLB) test applied at four different points of the specimen and the acoustic signals emitted by the damage were acquired and processed by Hinkley and Akaike criteria. The results indicate that, although both signal processing methodologies were able to determine the damage location, Akaike presented higher precision when compared to Hinkley approach. Moreover, the experimental results indicated that the low-cost piezoelectric sensors have a great potential to be applied in the location of structural failures.
Temperature measurement of stored biomass using low-frequency acoustic waves and correlation signal processing techniques
