Application of a Particle Filter to Flaw Identification for Ultrasonic Nondestructive Evaluation: Assimilation of Simulated and Measured Data

The assimilation of simulated and measured data is essential for advancing technology in NDE 4.0. In this study, a particle filter (PF) was applied to identify the geometry of flaws for ultrasonic nondestructive testing. A PF based on a probabilistic approach that allows errors in measurement and simulation models may be of great assistance for the data assimilation. In the PF, state variables are expressed by random data samples called particles, together with their associated weights. The PF estimates the probabilistic density function of the state variables by merging simulation data with measured data. Data types must be physically identical in the simulation and measurement to enhance the accuracy and to accelerate the convergence speed in the PF. Here, the scattering component, which is specific information related to the flaw geometry, was used for the likelihood evaluation in the PF. The simulation, which needed many particles, was conducted using the elastodynamic finite integration technique accelerated by parallel computing with graphics processing units. The proposed PF approach was demonstrated in ultrasonic measurement, and the geometries of artificial flaws in aluminum specimens were identified using only one pulse-echo signal at a single transducer.

Automated summary

The study applies a particle filter (PF) to identify flaw geometry in ultrasonic nondestructive testing, merging simulation data with measured data using a probabilistic approach to deal with errors and enhance accuracy and convergence speed.