Data-driven damage diagnosis under environmental and operational variability by novel statistical pattern recognition methods

Abstract

Feature extraction by time-series analysis and decision making through distance-based methods are powerful and efficient statistical pattern recognition techniques for data-driven structural health monitoring. The motivation of this article is to propose an innovative residual-based feature extraction approach based on AutoRegressive modeling and a novel statistical distance method named as Partition-based Kullback–Leibler Divergence for damage detection and localization by using randomly high-dimensional damage-sensitive features under environmental and operational variability. The key novel element of the proposed feature extraction approach is to establish a two-stage offline and online learning algorithms for extracting the residuals of AutoRegressive model as the main damage-sensitive features. This technique brings the great benefit of reducing the computational time and storage space for feature extraction in long-term monitoring conditions. The major contribution of Partition-based Kullback–Leibler Divergence method is to exploit a partitioning strategy for dividing random features into individual partitions and utilize numerical information of partitioning in distance calculation rather than directly applying random samples. Dealing with the major challenging issue of using the high-dimensional features in decision making and applicability to both correlated and uncorrelated random datasets are the main advantages of Partition-based Kullback–Leibler Divergence method. The accuracy and reliability of the proposed approaches are experimentally validated by two well-known benchmark structures. The stationarity and linearity of measured vibration responses for using in AutoRegressive modeling are evaluated by two hypothesis tests. Comparative studies are also conducted to demonstrate the superiority of the proposed methods over some exciting state-of-the-art techniques. Results show that the methods presented here succeed in detecting and locating damage and make time-saving and efficient tools for feature extraction and damage diagnosis.

Unsupervised deep learning approach using a deep auto-encoder with a one-class support vector machine to detect damage

“…1 Data-driven damage detection methods can provide a reliable solution to detect structural damage. [10][11][12][13] In the past decades, many data-driven methods using machine learning techniques have been developed for structural damage detection. [14][15][16] Feature extraction is typically a key element in these developed damage detection methods, and extracting appropriate damage-sensitive features is one of the critical components of specific damage detection problems.…”

Ensemble learning‐based structural health monitoring by Mahalanobis distance metrics

“…Due to the direct use of measured data, the modern or data‐based methods are usually developed from the concept of statistical pattern recognition. Feature extraction and statistical decision making are two key parts of any data‐based method that significantly affect the SHM results 9 …”

Output-Only Damage Detection of Shear Building Structures Using an Autoregressive Model-Enhanced Optimal Subpattern Assignment Metric

“…Additionally, to deal with the randomly high-dimensional damage-sensitive features under environmental and operational variability, Entezami et al [46] proposed a feature extraction approach based on an AR model residual and a statistical distance method named partition-based Kullback Leibler Divergence (KLD) for damage detection. Zhou et al [47] combined a vector time-dependent autoregressive (TAR) model with a least squares support vector machine to identify the structural parameters for linear time-varying structural systems.…”