A flexible factor analysis based on the class of mean-mixture of normal distributions

Hashemi, Farzane; Naderi, Mehrdad; Jamalizadeh, Ahad; Bekker, Andriëtte

doi:10.1016/j.csda.2020.107162

Cited by 5 publications

(4 citation statements)

References 33 publications

(50 reference statements)

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“…The classical factor analysis is suitable for multivariate data when the dimension of sampling is high; in other words, this technique is applicable for dimensionality reduction, further than for data normalization. A main limitation of this approach may be the size of the multivariate data: in most cases, it is assumed that data of interest are high-dimensional, with a normality property of the factors [ 51 ]. It may then become problematic to estimate a covariance matrix when the multivariate data of interest are low-dimensional with a non-Gaussian distribution.…”

Section: Proposed Multi-level Machine Learning Methodsmentioning

confidence: 99%

“…In probability theory, MCMC is a computer-driven sampling method that allows the characterization of a probability distribution model by randomly sampling values out of the distribution of interest, without a thorough knowledge of its mathematical properties [ 51 ]. The term “Monte Carlo” refers to the practice of estimating the properties of a distribution by examining random samples obtained from the distribution of interest.…”

Section: Proposed Multi-level Machine Learning Methodsmentioning

confidence: 99%

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Damage Detection in Largely Unobserved Structures under Varying Environmental Conditions: An AutoRegressive Spectrum and Multi-Level Machine Learning Methodology

Entezami

Mariani

Shariatmadar

2022

Sensors

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Vibration-based damage detection in civil structures using data-driven methods requires sufficient vibration responses acquired with a sensor network. Due to technical and economic reasons, it is not always possible to deploy a large number of sensors. This limitation may lead to partial information being handled for damage detection purposes, under environmental variability. To address this challenge, this article proposes an innovative multi-level machine learning method by employing the autoregressive spectrum as the main damage-sensitive feature. The proposed method consists of three levels: (i) distance calculation by the log-spectral distance, to increase damage detectability and generate distance-based training and test samples; (ii) feature normalization by an improved factor analysis, to remove environmental variations; and (iii) decision-making for damage localization by means of the Jensen–Shannon divergence. The major contributions of this research are represented by the development of the aforementioned multi-level machine learning method, and by the proposal of the new factor analysis for feature normalization. Limited vibration datasets relevant to a truss structure and consisting of acceleration time histories induced by shaker excitation in a passive system, have been used to validate the proposed method and to compare it with alternate, state-of-the-art strategies.

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Section: Proposed Multi-level Machine Learning Methodsmentioning

confidence: 99%

Section: Proposed Multi-level Machine Learning Methodsmentioning

confidence: 99%

Damage Detection in Largely Unobserved Structures under Varying Environmental Conditions: An AutoRegressive Spectrum and Multi-Level Machine Learning Methodology

Entezami

Mariani

Shariatmadar

2022

Sensors

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show abstract

“…The augmented data samples are applied to visualize the EOV. In probability theory, MCMC is a computer-driven sampling method that allows one to characterize a probability distribution model by randomly sampling values out of the distribution of interest without any entire knowledge about its mathematical properties [41]. The term "Monte Carlo" is the practice of estimating the properties of a distribution by examining random samples from the distribution of interest.…”

Section: Data Augmentation By Mcmcmentioning

confidence: 99%

Online Hybrid Learning Methods for Real-Time Structural Health Monitoring Using Remote Sensing and Small Displacement Data

et al. 2022

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Structural health monitoring (SHM) by using remote sensing and synthetic aperture radar (SAR) images is a promising approach to assessing the safety and the integrity of civil structures. Apart from this issue, artificial intelligence and machine learning have brought great opportunities to SHM by learning an automated computational model for damage detection. Accordingly, this article proposes online hybrid learning methods to firstly deal with some major challenges in data-driven SHM and secondly detect damage via small displacement data from SAR images in a real-time manner. The proposed methods contain three main parts: (i) data augmentation by Hamiltonian Monte Carlo and slice sampling for addressing the problem of small displacement data, (ii) data normalization by an online deep transfer learning algorithm for removing the effects of environmental and/or operational variability from augmented data, and (iii) feature classification via a scalar novelty score. The major contributions of this research include proposing two online hybrid unsupervised learning methods and providing effective frameworks for online damage detection. A small set of displacement samples extracted from SAR images of TerraSar-X regarding a long-term monitoring scheme of the Tadcaster Bridge in United Kingdom is applied to validate the proposed methods.

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“…In statistics and the theory of probability, MCMC is a statistical technique for sampling data points by characterizing a probability distribution function. This technique simulates random data samples without any knowledge about its mathematical characteristics [32]. The word "Monte Carlo" makes sense of estimating the properties of a distribution function based on the examination of random data points.…”

Section: Data Augmentation By Markov Chain Monte Carlomentioning

confidence: 99%

Detection of Partially Structural Collapse Using Long-Term Small Displacement Data from Satellite Images

Entezami

Michele

Arslan

et al. 2022

Sensors

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The development of satellite sensors and interferometry synthetic aperture radar (InSAR) technology has enabled the exploitation of their benefits for long-term structural health monitoring (SHM). However, some restrictions cause this process to provide a small number of images leading to the problem of small data for SAR-based SHM. Conversely, the major challenge of the long-term monitoring of civil structures pertains to variations in their inherent properties by environmental and/or operational variability. This article aims to propose new hybrid unsupervised learning methods for addressing these challenges. The methods in this work contain three main parts: (i) data augmentation by the Markov Chain Monte Carlo algorithm, (ii) feature normalization, and (iii) decision making via Mahalanobis-squared distance. The first method presented in this work develops an artificial neural network-based feature normalization by proposing an iterative hyperparameter selection of hidden neurons of the network. The second method is a novel unsupervised teacher–student learning by combining an undercomplete deep neural network and an overcomplete single-layer neural network. A small set of long-term displacement samples extracted from a few SAR images of TerraSAR-X is applied to validate the proposed methods. The results show that the methods can effectively deal with the major challenges in the SAR-based SHM applications.

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A flexible factor analysis based on the class of mean-mixture of normal distributions

Cited by 5 publications

References 33 publications

Damage Detection in Largely Unobserved Structures under Varying Environmental Conditions: An AutoRegressive Spectrum and Multi-Level Machine Learning Methodology

Damage Detection in Largely Unobserved Structures under Varying Environmental Conditions: An AutoRegressive Spectrum and Multi-Level Machine Learning Methodology

Online Hybrid Learning Methods for Real-Time Structural Health Monitoring Using Remote Sensing and Small Displacement Data

Detection of Partially Structural Collapse Using Long-Term Small Displacement Data from Satellite Images

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