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Journal ArticleDOI

Emergent damage pattern recognition using immune network theory

01 Jul 2011-Smart Structures and Systems (Techno-Press)-Vol. 8, Iss: 1, pp 69-92
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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Citations
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Journal ArticleDOI
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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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

Journal ArticleDOI
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

References
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Book ChapterDOI
13 Sep 2004
TL;DR: A real-valued Negative Selection Algorithm for fault detection in man-in-the-loop aircraft operation, using body-axes angular rate sensory data exhibiting the normal flight behavior patterns, to generate probabilistically a set of fault detectors.
Abstract: We investigated a real-valued Negative Selection Algorithm (NSA) for fault detection in man-in-the-loop aircraft operation. The detection algorithm uses body-axes angular rate sensory data exhibiting the normal flight behavior patterns, to generate probabilistically a set of fault detectors that can detect any abnormalities (including faults and damages) in the behavior pattern of the aircraft flight. We performed experiments with datasets (collected under normal and various simulated failure conditions) using the NASA Ames man-in-the-loop high-fidelity C-17 flight simulator. The paper provides results of experiments with different datasets representing various failure conditions.

214 citations


"Emergent damage pattern recognition..." refers background in this paper

  • ...(Dasgupta et al. 2004) investigate a real-value immune negative selection algorithm for aircraft fault detection....

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Journal ArticleDOI
TL;DR: A novel approach to finding the number of clusters in data based on the minimization of a regularized cost function that identifies the neighborhood as a scale parameter and obtains thenumber of cluster centers at varying values of the scale parameter.

162 citations


"Emergent damage pattern recognition..." refers methods in this paper

  • ...Various methods to determine the best number of clusters are discussed in (Kothari and Pitts 1999, Tibshirani et al. 2001, Sugar and James 2003, Salvador and Chan 2004, Cheong and Lee 2008, Qinpei et al. 2008)....

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Journal ArticleDOI
TL;DR: It is shown that UAIC is an adaptive clustering algorithm, which outperforms other algorithms in all the three experiments the authors carried out, and possesses biological properties such as clonal selection, immune network, and immune memory.
Abstract: A new method in computational intelligence namely artificial immune systems (AIS), which draw inspiration from the vertebrate immune system, have strong capabilities of pattern recognition. Even though AIS have been successfully utilized in several fields, few applications have been reported in remote sensing. Modern commercial imaging satellites, owing to their large volume of high-resolution imagery, offer greater opportunities for automated image analysis. Hence, we propose a novel unsupervised machine-learning algorithm namely unsupervised artificial immune classifier (UAIC) to perform remote sensing image classification. In addition to their nonlinear classification properties, UAIC possesses biological properties such as clonal selection, immune network, and immune memory. The implementation of UAIC comprises two steps: initially, the first clustering centers are acquired by randomly choosing from the input remote sensing image. Then, the classification task is carried out. This assigns each pixel to the class that maximizes stimulation between the antigen and the antibody. Subsequently, based on the class, the antibody population is evolved and the memory cell pool is updated by immune algorithms until the stopping criterion is met. The classification results are evaluated by comparing with four known algorithms: K-means, ISODATA, fuzzy K-means, and self-organizing map. It is shown that UAIC is an adaptive clustering algorithm, which outperforms other algorithms in all the three experiments we carried out.

161 citations


"Emergent damage pattern recognition..." refers methods in this paper

  • ...For example, Zhong et al. (Zhong et al. 2006) employ an AIS-based unsupervised machine-learning algorithm to perform remote sensing image classification....

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Journal ArticleDOI
TL;DR: An extensive critical review of the current literature on AIS for data mining, focusing on the data mining tasks of classification and anomaly detection and several important lessons to be taken from the natural immune system are discussed.
Abstract: This paper advocates a problem-oriented approach for the design of artificial immune systems (AIS) for data mining. By problem-oriented approach we mean that, in real-world data mining applications the design of an AIS should take into account the characteristics of the data to be mined together with the application domain: the components of the AIS - such as its representation, affinity function, and immune process - should be tailored for the data and the application. This is in contrast with the majority of the literature, where a very generic AIS algorithm for data mining is developed and there is little or no concern in tailoring the components of the AIS for the data to be mined or the application domain. To support this problem-oriented approach, we provide an extensive critical review of the current literature on AIS for data mining, focusing on the data mining tasks of classification and anomaly detection. We discuss several important lessons to be taken from the natural immune system to design new AIS that are considerably more adaptive than current AIS. Finally, we conclude this paper with a summary of seven limitations of current AIS for data mining and ten suggested research directions.

148 citations


"Emergent damage pattern recognition..." refers background in this paper

  • ...1(b) (De Castro and Timmis 2002)....

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  • ...Following this recognizing scheme, an immune network of interaction is formed (Timmis et al. 2008)....

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  • ...Freitas and Timmis (Freitas and Timmis 2007) use a problem-oriented approach for the design of AIS for data mining....

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  • ...The continuous recruitment and elimination of memory cells not only provides a competition mechanism to control the survival of memory cells in the network, but also offers great potential to discover memory cells which are able to bind with unpredictable invaders (new data patterns) (De Castro and Timmis 2002)....

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  • ...The artificial immune systems can be defined as the abstract or metaphorical computational systems developed using ideas, theories, and components, extracted from the natural immune system (De Castro and Timmis 2002)....

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Journal ArticleDOI
TL;DR: The effects of time synchronization error and data loss are investigated, aiming to clarify requirements on synchronization accuracy and communication reliability in SHM applications and Coordinated computing is examined as a way to manage large amounts of data.
Abstract: Smart sensors densely distributed over structures can provide rich information for structural monitoring using their onboard wireless communication and computational capabilities However, issues such as time synchronization error, data loss, and dealing with large amounts of harvested data have limited the implementation of full-fledged systems Limited network resources (eg battery power, storage space, bandwidth, etc) make these issues quite challenging This paper first investigates the effects of time synchronization error and data loss, aiming to clarify requirements on synchronization accuracy and communication reliability in SHM applications Coordinated computing is then examined as a way to manage large amounts of data

144 citations


"Emergent damage pattern recognition..." refers background in this paper

  • ...(Nagayama et al. 2007) investigate the effects of time synchronization accuracy and communication reliability in SHM applications and examine coordinated computing for the management of large amount of SHM data....

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