Deterministically generated negative selection algorithm for damage detection in civil engineering systems

Barontini, Alberto; Perera, Ricardo; Masciotta, Maria Giovanna; Amado-Mendes, Paulo; Ramos, Luís F.

doi:10.1016/j.engstruct.2019.109444

Cited by 15 publications

(7 citation statements)

References 20 publications

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“…algorithm is based on the immune mechanism of human cells in the process of development, and the basic idea of the algorithm is to generate the detector set from the normal data set, and then use the detector to detect the state of the system or equipment [10,11].…”

Section: Negative Selection Algorithm Negative Selectionmentioning

confidence: 99%

“…When the detector is generated based on GPSO, the determination of fitness function is an important step. From formula ( 6) to (10) and the GPSO, it can be seen that in the detector generation stage, the comprehensive correlation degree of the training sample set and the detector set is less than zero. The smaller the value is, the more the resultant detector belongs to the non-self set.…”

Section: Detector Generation Strategymentioning

confidence: 99%

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Abnormal Detection of Pumping Unit Bearing Based on Extension Theory

Zhang

2021

IEEJ Transactions Elec Engng

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In order to solve the problem that is difficult to extract the fault data of bearing of pumping unit in operation, a negative selection algorithm based on extension theory is proposed to detect the abnormal of bearing. First, a negative selection algorithm detector set model is constructed by using matter element. Synthetic correlation degree function is used as the matching rule of detector generation stage and bearing fault anomaly detection stage. Genetic Particle Swarm Optimization (GPSO) is used to generate detector set. Aiming at the problem of large redundancy of the detector set, the correlation function is used to formulate merging rules to merge the detector set interval. Finally, bearing anomaly detection is carried out by using the bearing fault data of Case Western Reserve University. The results show that the activation rate of the inner ring fault detector is 98.89%, the activation rate of the outer ring fault detector is 98.61%, and the activation rate of the ball fault detector is 99.17%.

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Section: Negative Selection Algorithm Negative Selectionmentioning

confidence: 99%

Section: Detector Generation Strategymentioning

confidence: 99%

Abnormal Detection of Pumping Unit Bearing Based on Extension Theory

Zhang

2021

IEEJ Transactions Elec Engng

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“…In the context of SHM, damage detection can be formulated as a pattern recognition problem addressed through machine learning algorithms [51]. In many civil engineering applications, the lack of information on the system response in a condition that differs from the reference one does reduce the damage detection problem to one-class classification [7], the goal being to associate the measured features of the system to one of the two possible condition states, e.g., safe/undamaged or unsafe/damaged, by training on baseline samples belonging to the safe state only. Machine learning algorithms may require a rescaling of the analysed features to a predefined range [52], for instance to train and implement a classifier within a unitary feature space, U = [0, 1] n , where n is the number of features analysed (in the case of vibration-based damage detection, the features are derived from vibration signals).…”

Section: Vibration-based Damage Detectionmentioning

confidence: 99%

“…The classifier is a function trained through a dataset from the reference state of the system. Among the algorithms suitable to address such problems, the authors have recently investigated the applicability of Negative Selection Algorithms (NSAs), developing a deterministic generation based version that analyses the evolution of pairs of features at the same time [7][8][9]. The feature selection strategy that will be described in Section 2 is tailored to this new version of the NSA and it is based on the pairwise correlation of structural properties between themselves and with non-structural factors.…”

Section: Introductionmentioning

confidence: 99%

Reducing the Training Samples for Damage Detection of Existing Buildings through Self-Space Approximation Techniques

Barontini

Masciotta

Amado-Mendes

et al. 2021

Sensors

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Data-driven methodologies are among the most effective tools for damage detection of complex existing buildings, such as heritage structures. Indeed, the historical evolution and actual behaviour of these assets are often unknown, no physical models are available, and the assessment must be performed only based on the tracking of a set of damage-sensitive features. Selecting the most representative state indicators to monitor and sampling them with an adequate number of records are therefore essential tasks to guarantee the successful performance of the damage detection strategy. Despite their relevance, these aspects have been frequently taken for granted and little attention has been paid to them by the scientific community working in the field of Structural Health Monitoring. The present paper aims to fill this gap by proposing a multistep strategy to drive the selection of meaningful pairs of correlated features in order to support the damage detection as a one-class classification problem. Numerical methods to reduce the number of necessary acquisitions and estimate the performance of approximation techniques are also provided. The analyses carried out to test and validate the proposed strategy exploit a dense dataset collected during the long-term monitoring of an outstanding heritage structure, i.e., the Church of ‘Santa Maria de Belém’ in Lisbon.

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“…Over the past few decades, vibration-based methods have been developed for structural damage identification [ 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. The underlying idea behind these methods comes from the fact that modal parameters are linked to physical parameters of the structure.…”

Section: Introductionmentioning

confidence: 99%

Visible Particle Series Search Algorithm and Its Application in Structural Damage Identification

Mohebian

Aval

Noori

et al. 2022

Sensors

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Identifying structural damage is an essential task for ensuring the safety and functionality of civil, mechanical, and aerospace structures. In this study, the structural damage identification scheme is formulated as an optimization problem, and a new meta-heuristic optimization algorithm, called visible particle series search (VPSS), is proposed to tackle that. The proposed VPSS algorithm is inspired by the visibility graph technique, which is a technique used basically to convert a time series into a graph network. In the proposed VPSS algorithm, the population of candidate solutions is regarded as a particle series and is further mapped into a visibility graph network to obtain visible particles. The information captured from the visible particles is then utilized by the algorithm to seek the optimum solution over the search space. The general performance of the proposed VPSS algorithm is first verified on a set of mathematical benchmark functions, and, afterward, its ability to identify structural damage is assessed by conducting various numerical simulations. The results demonstrate the high accuracy, reliability, and computational efficiency of the VPSS algorithm for identifying the location and the extent of damage in structures.

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Deterministically generated negative selection algorithm for damage detection in civil engineering systems

Cited by 15 publications

References 20 publications

Abnormal Detection of Pumping Unit Bearing Based on Extension Theory

Abnormal Detection of Pumping Unit Bearing Based on Extension Theory

Reducing the Training Samples for Damage Detection of Existing Buildings through Self-Space Approximation Techniques

Visible Particle Series Search Algorithm and Its Application in Structural Damage Identification

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