2 research outputs found
Signal Processing for NDE
Nowadays, testing and evaluating of industrial equipment using nondestructive tests, is a
fundamental step in the manufacturing process. The complexity and high costs of manufacturing
industrial components, require examinations in some way about the quality and reliability of the
specimens. However, it should be noted, that in order to accurately perform the nondestructive
test, in addition to theoretical knowledge, it is also essential to have the experience and carefulness,
which requires special courses and experience with theoretical education. Therefore, in the
traditional methods, which are based on manual testing techniques and the test results depend on
the operator, there is the possibility of an invalid inference from the test data. In other words, the
accuracy of conclusion from the obtained data is dependent on the skill and experience of the
operator. Thus, using the signal processing techniques for nondestructive evaluation (NDE), it is
possible to optimize the methods of nondestructive inspection, and in other words, to improve the
overall system performance, in terms of reliability and system implementation costs.
In recent years, intelligent signal processing techniques have had a significant impact on the
progress of nondestructive assessment. In other words, by automating the processing of
nondestructive data and signals, and using the artificial intelligence methods, it is possible to
optimize nondestructive inspection methods. Hence, improve overall system performance in terms
of reliability and Implementation costs of the system. This chapter reviews the issues of intelligent
processing of nondestructive testing (NDT) signals
Improved versions of the bees algorithm for global optimisation
This research focuses on swarm-based optimisation algorithms, specifically the Bees Algorithm. The Bees Algorithm was inspired by the foraging behaviour of honey bees in nature. It employs a combination of exploration and exploitation to find the solutions of optimisation problems. This thesis presents three improved versions of the Bees Algorithm aimed at speeding up its operation and facilitating the location of the global optimum. For the first improvement, an algorithm referred to as the Nelder and Mead Bees Algorithm (NMBA) was developed to provide a guiding direction during the neighbourhood search stage. The second improved algorithm, named the recombination-based Bees Algorithm (rBA), is a variant of the Bees Algorithm that utilises a recombination operator between the exploited and abandoned sites to produce new candidates closer to optimal solutions. The third improved Bees Algorithm, called the guided global best Bees Algorithm (gBA), introduces a new neighbourhood shrinking strategy based on the best solution so far for a more effective exploitation search and develops a new bee recruitment mechanism to reduce the number of parameters.
The proposed algorithms were tested on a set of unconstrained numerical functions and constrained mechanical engineering design problems. The performance of the algorithms was compared with the standard Bees Algorithm and other swarm based algorithms. The results showed that the improved Bees Algorithms performed better than the standard Bees Algorithm and other algorithms on most of the problems tested. Furthermore, the algorithms also involve no additional parameters and a reduction on the number of parameters as well