Efficient generation of realistic guided wave signals for reliability estimation

Across nondestructive testing and structural health monitoring (SHM), accurate knowledge of the systems’ reliability for detecting defects, such as probability of detection (POD) analysis is essential to enabling widespread adoption. Traditionally, this relies on access to extensive experimental data to cover all critical areas of the parametric space, which becomes expensive, and heavily undermines the benefit such systems bring. In response to these challenges, reliability estimation based on numerical simulation emerges as a practical solution, offering enhanced efficiency and cost-effectiveness. Nevertheless, precise reliability estimation demands that the simulated data faithfully represents the real-world performance. In this context, a numerical framework tailored to generate realistic signals for reliability estimation purposes is presented here, focusing on the application of guided wave SHM for pipe monitoring. It specifically incorporates key characteristics of real signals: random noise and coherent noise caused by the imbalance in transducer performance within guided wave monitoring systems. The effectiveness of our proposed methodology is demonstrated through a comprehensive comparative analysis between simulation-generated signals and experimental signals both individually and statistically. Furthermore, to assess the reliability of a guided wave system in terms of the inspection range for pipe monitoring, a series of POD analyses using simulation-generated data were conducted. The comparison of POD curves derived from ideal and realistic simulation data underscores the necessity of considering coherent noise for accurate POD curve calculations. Moreover, the POD analysis based on realistic simulation-generated data provides a quantitative estimation of the inspection range with more details compared to the current industry practice. Our presented framework offers a pioneering approach to generate realistic guided wave signals, thereby facilitating the practical assessment of the reliability of guided wave monitoring systems. This advancement also has the potential to effectively address challenges related to data scarcity in broader applications requiring high-fidelity data, such as the training of machine learning models for damage identification from complex signals for all aspects of ultrasonic inspections with both guided and bulk waves.