An overview on structural health monitoring: From the current state-of-the-art to new bio-inspired sensing paradigms

In the last decades, the field of structural health monitoring (SHM) has grown exponentially. Yet, several technical constraints persist, which are preventing full realization of its potential. To upgrade current state-of-the-art technologies, researchers have started to look at nature’s creations giving rise to a new field called ‘biomimetics’, which operates across the border between living and non-living systems. The highly optimised and time-tested performance of biological assemblies keeps on inspiring the development of bio-inspired artificial counterparts that can potentially outperform conventional systems. After a critical appraisal on the current status of SHM, this paper presents a review of selected works related to neural, cochlea and immune-inspired algorithms implemented in the field of SHM, including a brief survey of the advancements of bio-inspired sensor technology for the purpose of SHM. In parallel to this engineering progress, a more in-depth understanding of the most suitable biological patterns to be transferred into multimodal SHM systems is fundamental to foster new scientific breakthroughs. Hence, grounded in the dissection of three selected human biological systems, a framework for new bio-inspired sensing paradigms aimed at guiding the identification of tailored attributes to transplant from nature to SHM is outlined.info:eu-repo/semantics/acceptedVersio

Monitoring and Control Framework for Advanced Power Plant Systems Using Artificial Intelligence Techniques

This dissertation presents the design, development, and simulation testing of a monitoring and control framework for dynamic systems using artificial intelligence techniques. A comprehensive monitoring and control system capable of detecting, identifying, evaluating, and accommodating various subsystem failures and upset conditions is presented. The system is developed by synergistically merging concepts inspired from the biological immune system with evolutionary optimization algorithms and adaptive control techniques.;The proposed methodology provides the tools for addressing the complexity and multi-dimensionality of the modern power plants in a comprehensive and integrated manner that classical approaches cannot achieve. Current approaches typically address abnormal condition (AC) detection of isolated subsystems of low complexity, affected by specific AC involving few features with limited identification capability. They do not attempt AC evaluation and mostly rely on control system robustness for accommodation. Addressing the problem of power plant monitoring and control under AC at this level of completeness has not yet been attempted.;Within the proposed framework, a novel algorithm, namely the partition of the universe, was developed for building the artificial immune system self. As compared to the clustering approach, the proposed approach is less computationally intensive and facilitates the use of full-dimensional self for system AC detection, identification, and evaluation. The approach is implemented in conjunction with a modified and improved dendritic cell algorithm. It allows for identifying the failed subsystems without previous training and is extended to address the AC evaluation using a novel approach.;The adaptive control laws are designed to augment the performance and robustness of baseline control laws under normal and abnormal operating conditions. Artificial neural network-based and artificial immune system-based approaches are developed and investigated for an advanced power plant through numerical simulation.;This dissertation also presents the development of an interactive computational environment for the optimization of power plant control system using evolutionary techniques with immunity-inspired enhancements. Several algorithms mimicking mechanisms of the immune system of superior organisms, such as cloning, affinity-based selection, seeding, and vaccination are used. These algorithms are expected to enhance the computational effectiveness, improve convergence, and be more efficient in handling multiple local extrema, through an adequate balance between exploration and exploitation.;The monitoring and control framework formulated in this dissertation applies to a wide range of technical problems. The proposed methodology is demonstrated with promising results using a high validity DynsimRTM model of the acid gas removal unit that is part of the integrated gasification combined cycle power plant available at West Virginia University AVESTAR Center. The obtained results show that the proposed system is an efficient and valuable technique to be applied to a real world application. The implementation of this methodology can potentially have significant impacts on the operational safety of many complex systems

Artificial immune system for solving global optimization problems

En este trabajo, se presenta un nuevo modelo de Sistema Inmune Artificial (SIA) basado en los procesos que sufren las células T para resolver problemas de optimización global. El modelo, denominado MCT, trabaja sobre cuatro poblaciones con diferentes representaciones para las células y cada población atraviesa por distintos procesos. Se validó el modelo con 23 funciones tomadas de la literatura especializada. El modelo es comparado con diferentes enfoques bio-inspirados.In this paper, we present a novel model of an artificial immune system (AIS), based on the process that suffers the T-Cell. The proposed model is used for global optimization problems. The model operates on four populations: Virgins, Effectors (CD4 and CD8) and Memory. Each of them has a different role, representation and procedures. We validate our proposed approach with a set of test functions taken from the specialized literature and we also compare our results with the results obtained by different bio-inspired approachesWorkshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

A comparison of immune responses and disease resistance in clonal lines of Nile tilapia Oreochromis niloticus L.

Five clonal lines of Nile tilapia Oreochromis niloticus were produced and propagated. Three second generation clonal lines (clonal lines A, B and C) were produced from females from the respective clonal lines by meiotic gynogenesis followed by sib mating between female and sex reversed neomales from the same clonal line. Clonal lines D and E were first generation clones produced by meiotic gynogenesis from an XX mitotic gynogenetic daughter of an XY neofemale. Survival rate at both pigmentation and yolk sac absorption stages were poor in the meiotic gynogenetics. No males were observed in any of the clonal lines. Microsatellite loci in multiplex reactions were used for parentage analysis of the clonal lines. Analysis of data revealed that clonal individuals from all clonal lines inherited only maternal alleles. The specific immune response were studied in Clonal lines A and B. an outbred clonal line OBC (AXB) and an unrelated control group of Nile tilapia following immunisation with SRBC, DNP-KLH and TNP-LPS. The specific immune response of clonal line A was significantly higher than clonal line B. The OBC (AXB) line had an intermediate response between clonal lines A and B, which was significantly higher than clonal line A and significantly lower than clonal line B. The response of URC was lower than clonal line A but the difference was not statistically significant. The experimental groups of fish had similar patterns of specific immune response after being vaccinated with heat killed Aeromonas hydrophila T4. The experimental challenge with A. hydrophila T4 revealed an inverse relationship between antibody production and susceptibility to disease. Clonal line A had significantly lower mortality than clonal line B. Mortality in OBC (AXB) was significantly higher than clonal line A and significantly lower than clonal line B. Lymphocytes isolated from peripheral blood, spleen and head kidney of the experimental groups of fish were stimulated with T -cell mitogen Con A and B-cell mitogen LPS. No correlation was found between polyclonal activation of lymphocytes of healthy fish and specific immune response or disease resistance in the same clonal line