Structural health monitoring using high-dimensional features from time series modeling by innovative hybrid distance-based methods

Daneshvar, Mohammad Hassan; Gharighoran, Alireza; Zareei, Seyed Alireza; Karamodin, Abbas

doi:10.1007/s13349-020-00466-5

Cited by 16 publications

(6 citation statements)

References 34 publications

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“…For comparison with damage classifiers based on amplitude-aware permutation entropy, we introduce a Kullback–Leibler divergence (KLD) [ 23 ] univariate damage classifier that utilizes statistical features of the model residuals. KLD describes an asymmetric measure of two probability distributions and can represent the deviations that occur in the probability distribution of a signal.…”

Section: Nonlinear Damage Diagnosis Based On Amplitude-aware Permutat...mentioning

confidence: 99%

“…This approach solves the problem of using high-dimensional damage features in statistical decision-making and enables early damage detection under various environmental and operational conditions. Daneshvar et al [ 23 ] employed Kullback–Leibler divergence (KLD) and the residual relative error (RRE) to dimensionality reduction of AR model residual and then combined the reduced features with the MD for damage diagnosis. In a subsequent study, Daneshvar et al [ 24 ] used a Gaussian mixture model to dimensionally reduce the AR-ARX model residuals and combined them with the MD for early damage diagnosis of cable-stayed bridges.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Structural Health Monitoring: Leveraging Amplitude-Aware Permutation Entropy of Time Series Model Residuals for Nonlinear Damage Diagnosis

Zhang,

Li,

2024

Sensors

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In structural health monitoring (SHM), most current methods and techniques are based on the assumption of linear models and linear damage. However, the damage in real engineering structures is more characterized by nonlinear behavior, including the appearance of cracks and the loosening of bolts. To solve the structural nonlinear damage diagnosis problem more effectively, this study combines the autoregressive (AR) model and amplitude-aware permutation entropy (AAPE) to propose a data-driven damage detection method. First, an AR model is built for the acceleration data from each structure sensor in the baseline state, including determining the model order using a modified iterative method based on the Bayesian information criterion (BIC) and calculating the model coefficients. Subsequently, in the testing phase, the residuals of the AR model are extracted as damage-sensitive features (DSFs), and the AAPE is calculated as a damage classifier to diagnose the nonlinear damage. Numerical simulation of a six-story building model and experimental data from a three-story frame structure at the Los Alamos Laboratory are utilized to illustrate the effectiveness of the proposed methodology. In addition, to demonstrate the advantages of the present method, we analyzed AAPE in comparison with other advanced univariate damage classifiers. The numerical and experimental results demonstrate the proposed method’s advantages in detecting and localizing minor damage. Moreover, this method is applicable to distributed sensor monitoring systems.

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Section: Nonlinear Damage Diagnosis Based On Amplitude-aware Permutat...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-Driven Structural Health Monitoring: Leveraging Amplitude-Aware Permutation Entropy of Time Series Model Residuals for Nonlinear Damage Diagnosis

Zhang,

Li,

2024

Sensors

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“…To this aim, the most relevant techniques are statistical distance measures, which may depend upon the type of damage-sensitive features to handle. Some of the useful univariate and multivariate distance techniques to mention include the Mahalanobis distance [ 20 , 21 , 22 ] and Kullback–Leibler divergence [ 15 , 23 , 24 ], dynamic time warping [ 25 ], and other damage indices based on relative errors [ 26 , 27 ], classical and robust multidimensional scaling algorithms [ 28 , 29 ], etc.…”

Section: Introductionmentioning

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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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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“…However, so far, most of the developments on the intelligent health monitoring technology based on optical fiber sensors have been made on small size structures. For next generation mechanical structural health monitoring systems applied to largescale structures, large-scale density sensor networks are required to be adopted to monitor different mechanical structure parameters, such as stress, strain, displacement, acoustic, pressure, and temperature [5]. Density sensors use different theories and have different functions [6].…”

Section: Introductionmentioning

confidence: 99%

Delphi Collaboration Strategy for Multiagent Optical Fiber Intelligent Health Monitoring System

et al. 2022

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Optical fiber sensors are very attractive in mechanical structure intelligent health monitoring system due to some unique characteristics, such as immunity to electromagnetic interference and to aggressive environments, high sensitive and fast response, small physical dimension, excellent resolution and range, and so on. For improving the accuracy and reliability of the optical fiber intelligent health monitoring system in practical engineering application, the collaboration and decision-making strategy based on Delphi method for multiagent optical fiber intelligent health monitoring system is studied in this paper. The proposed system is mainly composed of optical fiber sensing agent, intelligent evaluation agent, and system collaborative decision-making agent. The intelligent evaluation agent is used to evaluate the health status of the monitored mechanical structures. Delphi method is used by the system collaborative decision-making agent to consult each intelligent evaluation agent. Meanwhile, the collaborative partner selection algorithm is used to select the intelligent evaluation agent participating in the collaboration, and the intelligent evaluation agent that does not participate in the decision-making is dynamically modified by the decision result. The experiment for an aircraft wing box as the typical engineering structure is carried out and the verification system is designed, the decision result is compared with that without dynamic correction of the evaluation result. The comparative results indicate that the evaluation accuracy and reliability of the monitored mechanical structural damage are improved significantly after multiple rounds of collaboration and decision making.

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Structural health monitoring using high-dimensional features from time series modeling by innovative hybrid distance-based methods

Cited by 16 publications

References 34 publications

Data-Driven Structural Health Monitoring: Leveraging Amplitude-Aware Permutation Entropy of Time Series Model Residuals for Nonlinear Damage Diagnosis

Data-Driven Structural Health Monitoring: Leveraging Amplitude-Aware Permutation Entropy of Time Series Model Residuals for Nonlinear Damage Diagnosis

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

Delphi Collaboration Strategy for Multiagent Optical Fiber Intelligent Health Monitoring System

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