Super resolution array imaging of embedded defects within safety-critical components

There is a constant drive within the nuclear power industry to improve upon the characterization capabilities of ultrasonic Non-Destructive Evaluation (NDE) inspection techniques in order to improve safety and reduce costs, with particular emphasis placed on the ability to characterize small defects. The usage of ultrasonic phased array technologies have led to significant advancements in NDT performance relative to conventional monolithic transducers and they have also led to the development of several advanced imaging algorithms. A group of these called Super Resolution (SR) algorithms have been shown to demonstrate a capability to resolve scatterers separated by less than the diffraction limit when deployed in representative NDE inspections. In this thesis, the Factorisation Method (FM) and the Time Reversal Multiple Signal Classification (TR-MUSIC) algorithms were investigated in the imaging of embedded defects. The performance of these SR techniques in accurately characterising smooth embedded planar defects of varying size and orientations was investigated via two-dimensional (2D) Finite Element (FE) simulations and the results were experimentally validated. These studies were extended to consider more realistic three-dimensional (3D) smooth embedded planar defects in experimental trials and rough embedded planar defects, the latter being explored using 2D FE Monte Carlo simulations. The SR algorithms were also benchmarked against the conventional array Total Focusing Method, which is recognised to be a high performing and robust imaging technique. The SR algorithms were also applied to the imaging of 2D and 3D volumetric defects in order to determine if direct, image-based sizing could be achieved with these ultrasonic methods. The final and most challenging inspection case considered within this thesis was the inspection of embedded defects within austenitic stainless steel welds. These materials exhibit spatially-varying anisotropic coarse grained microstructures which can lead to significant ultrasonic signal attenuation and beam bending effects that make their NDE inspection difficult.Open Acces

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

A Review of the Family of Artificial Fish Swarm Algorithms: Recent Advances and Applications

The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming, following and random behaviors. Owing to a number of salient properties, which include flexibility, fast convergence, and insensitivity to the initial parameter settings, the family of AFSA has emerged as an effective Swarm Intelligence (SI) methodology that has been widely applied to solve real-world optimization problems. Since its introduction in 2002, many improved and hybrid AFSA models have been developed to tackle continuous, binary, and combinatorial optimization problems. This paper aims to present a concise review of the family of AFSA, encompassing the original ASFA and its improvements, continuous, binary, discrete, and hybrid models, as well as the associated applications. A comprehensive survey on the AFSA from its introduction to 2012 can be found in [1]. As such, we focus on a total of {\color{blue}123} articles published in high-quality journals since 2013. We also discuss possible AFSA enhancements and highlight future research directions for the family of AFSA-based models.Comment: 37 pages, 3 figure

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201