Smart structural health monitoring (SHM) system for on-board localization of defects in pipes using torsional ultrasonic guided waves

• Published in: Scientific reports Vol. 14; no. 1; pp. 24455 - 15
• Main Authors: Patil, Sheetal, Banerjee, Sauvik, Tallur, Siddharth
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 18.10.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/987; 639/766/930; Algorithms; Data acquisition; Data processing; Humanities and Social Sciences; Localization; multidisciplinary; Nondestructive testing; Pipes; Science; Science (multidisciplinary); Signal processing; Steel pipes; Transducers
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-76236-w

Most reported research for monitoring health of pipelines using ultrasonic guided waves (GW) typically utilize bulky piezoelectric transducer rings and laboratory-grade ultrasonic non-destructive testing (NDT) equipment. Consequently, the translation of these approaches from laboratory settings to field-deployable systems for real-time structural health monitoring (SHM) becomes challenging. In this work, we present an innovative algorithm for damage identification and localization in pipes, implemented on a compact FPGA-based smart GW-SHM system. The custom-designed board, featuring a Xilinx Artix-7 FPGA and front-end electronics, is capable of actuating the PZT thickness shear mode transducers, data acquisition and recording from PZT sensors and generating a damage index (DI) map for localizing the damage on the structure. The algorithm is a variation of the common source method adapted for cylindrical geometry. The utility of the algorithm is demonstrated for detection and localization of defects such as notch and mass loading on a steel pipe, through extensive finite element (FE) method simulations. Experimental results obtained using a C-clamp for applying mass loading on the pipe show good agreement with the FE simulations. The localization error values for experimental data analysed using C code on a processor implemented on the FPGA are consistent with algorithm results generated on a computer running Python code. The system presented in this study is suitable for a wide range of GW-SHM applications, especially in cost-sensitive scenarios that benefit from on-node signal processing over cloud-based solutions.