Emergent damage pattern recognition using immune network theory
TL;DR: The presented immune-network-based emergent pattern recognition (INEPR) algorithm can automatically generate an internal image mapping to the input data patterns without the need of specifying the number of patterns in advance.
Abstract: This paper presents an emergent pattern recognition approach based on the immune network theory and hierarchical clustering algorithms. The immune network allows its components to change and learn patterns by changing the strength of connections between individual components. The presented immune-network-based approach achieves emergent pattern recognition by dynamically generating an internal image for the input data patterns. The members (feature vectors for each data pattern) of the internal image are produced by an immune network model to form a network of antibody memory cells. To classify antibody memory cells to different data patterns, hierarchical clustering algorithms are used to create an antibody memory cell clustering. In addition, evaluation graphs and L method are used to determine the best number of clusters for the antibody memory cell clustering. The presented immune-network-based emergent pattern recognition (INEPR) algorithm can automatically generate an internal image mapping to the input data patterns without the need of specifying the number of patterns in advance. The INEPR algorithm has been tested using a benchmark civil structure. The test results show that the INEPR algorithm is able to recognize new structural damage patterns.
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TL;DR: A bioinspired strategy for the detection of structural changes using an artificial immune system (AIS) and a statistical data-driven modeling approach by means of a distributed piezoelectric active sensor network at different actuation phases is introduced.
Abstract: Among all the aspects that are linked to a structural health monitoring (SHM) system, algorithms, strategies, or methods for damage detection are currently playing an important role in improving the operational reliability of critical structures in several industrial sectors. This paper introduces a bioinspired strategy for the detection of structural changes using an artificial immune system (AIS) and a statistical data-driven modeling approach by means of a distributed piezoelectric active sensor network at different actuation phases. Damage detection and classification of structural changes using ultrasonic signals are traditionally performed using methods based on the time of flight. The approach followed in this paper is a data-based approach based on AIS, where sensor data fusion, feature extraction, and pattern recognition are evaluated. One of the key advantages of the proposed methodology is that the need to develop and validate a mathematical model is eliminated. The proposed methodology is applied, tested, and validated with data collected from two sections of an aircraft skin panel. The results show that the presented methodology is able to accurately detect damage.
31 citations
Cites methods from "Emergent damage pattern recognition..."
...In 2011, Chen and Zang [8] presented an algorithm based on immune network theory and hierarchical clustering algorithms....
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01 Jan 2015
9 citations
08 Jul 2014
TL;DR: A methodology for structural damage detection using a type of artificial intelligence that is called artificial immune system is presented and results show that the proposed methodology allows to detect damages in the experimental setup.
Abstract: Structural Health Monitoring is a growing area of interest given the benefits obtained from its use. This area includes different tasks in the damage identification process, the main important, is the damage detection since an early detection allows to avoid possible catastrophes in structures in service. Practical solutions require a big quantity of sensors and a robust system to process and obtain a reliable solution. In this sense, bio-inspired algorithms provide tools for an effective data analysis taking advantage of the developments provided by the nature by means of computational algorithms. As a contribution in this area, this paper presents a methodology for structural damage detection using a type of artificial intelligence that is called artificial immune system. The developed methodology includes the inspection of the structure by means of a distributed piezoelectric active sensor network at different actuation phases to define a baseline by each actuation phase using data from the structure when it is known as healthy. In a second step, same experiments are performed to the structure when its structural state is unknown to determine the presence of damage by using the developed artificial immune system. Results show that the proposed methodology allows to detect damages in the experimental setup.
9 citations
TL;DR: A framework for new bio-inspired sensing paradigms aimed at guiding the identification of tailored attributes to transplant from nature to SHM is outlined.
Abstract: In the last decades, the field of structural health monitoring (SHM) has grown exponentially. Yet, several technical constraints persist, which are preventing full realisation 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.
6 citations
01 Jan 2022
1 citations
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TL;DR: In this paper, the authors proposed a method called the "gap statistic" for estimating the number of clusters (groups) in a set of data, which uses the output of any clustering algorithm (e.g. K-means or hierarchical), comparing the change in within-cluster dispersion with that expected under an appropriate reference null distribution.
Abstract: We propose a method (the ‘gap statistic’) for estimating the number of clusters (groups) in a set of data. The technique uses the output of any clustering algorithm (e.g. K-means or hierarchical), comparing the change in within-cluster dispersion with that expected under an appropriate reference null distribution. Some theory is developed for the proposal and a simulation study shows that the gap statistic usually outperforms other methods that have been proposed in the literature.
4,283 citations
Book•
01 Jan 2000
TL;DR: The gap statistic is proposed for estimating the number of clusters (groups) in a set of data by comparing the change in within‐cluster dispersion with that expected under an appropriate reference null distribution.
Abstract: We propose a method (the ‘gap statistic’) for estimating the number of clusters (groups) in a set of data. The technique uses the output of any clustering algorithm (e.g. K-means or hierarchical), comparing the change in within-cluster dispersion with that expected under an appropriate reference null distribution. Some theory is developed for the proposal and a simulation study shows that the gap statistic usually outperforms other methods that have been proposed in the literature.
3,860 citations
Journal Article•
2,999 citations
"Emergent damage pattern recognition..." refers background in this paper
...The immune network theory (Jerne 1974) suggests that the immune system is composed of a regulated network of cells and molecules that recognize one another even in the absence of antigens....
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TL;DR: In this paper, a survey of the state-of-the-art structural health monitoring and damage detection tools for bridges is presented, including the use of signal processing, new sensors, and control theory.
Abstract: Increased awareness of the economic and social effects of aging, deterioration and extreme events on civil infrastructure has been accompanied by recognition of the need for advanced structural health monitoring and damage detection tools. Today, these tasks are done by visual inspection and very traditional methods such as the tap test. This labor-intensive task is done at a frequency of less than once every two years for bridges, and on an as-needed basis for other infrastructures such as buildings. Structural health monitoring techniques based on changes in dynamic characteristics have been studied for the last three decades. When the damage is substantial, these methods have some success in determining if damage has occurred. At incipient stages of damage, however, the existing methods are not as successful. A number of new research projects have been funded to improve the damage detection methods including the use of innovative signal processing, new sensors, and control theory. This survey paper hig...
927 citations
"Emergent damage pattern recognition..." refers background in this paper
...(Chang et al. 2003) review a number of research projects aiming to improve the damage detection methods including the use of novel signal processing, new sensors, and control theory....
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TL;DR: A simple, yet powerful nonparametric method for choosing the number of clusters based on distortion, a quantity that measures the average distance, per dimension, between each observation and its closest cluster center, is developed.
Abstract: One of the most difficult problems in cluster analysis is identifying the number of groups in a dataset. Most previously suggested approaches to this problem are either somewhat ad hoc or require parametric assumptions and complicated calculations. In this article we develop a simple, yet powerful nonparametric method for choosing the number of clusters based on distortion, a quantity that measures the average distance, per dimension, between each observation and its closest cluster center. Our technique is computationally efficient and straightforward to implement. We demonstrate empirically its effectiveness, not only for choosing the number of clusters, but also for identifying underlying structure, on a wide range of simulated and real world datasets. In addition, we give a rigorous theoretical justification for the method based on information-theoretic ideas. Specifically, results from the subfield of electrical engineering known as rate distortion theory allow us to describe the behavior of the dist...
761 citations