Structural condition assessment using entropy-based time series analysis

Abstract

We present a time-series-based algorithm to identify structural damage in the structure. The method is in the context of non-model-based approaches; hence, it eliminates the need of any representative numerical model of the structure to be built. The method starts by partitioning the state space into a finite number of subsets which are mutually exclusive and exhaustive and each subset is identified by a distinct symbol. Partitioning is performed based on a maximum entropy approach which takes into account the sparsity and distribution of information in the time series. After constructing the symbol space, the time series data are uniquely transformed from the state space into the constructed symbol space to create the symbol sequences. Symbol sequences are the simplified abstractions of the complex system and describe the evolution of the system. Each symbol sequence is statistically characterized by its entropy which is obtained based on the probability of occurrence of the symbols in the sequence. As a consequence of damage occurrence, the entropy of the symbol sequences changes; this change is implemented to define a damage indicative feature. The method shows promising results using data from two experimental case studies subject to varying excitation. The first specimen is a reinforced concrete jack arch which replicates one of the major structural components of the Sydney Harbor Bridge and the second specimen is a three-story frame structure model which has been tested at Los Alamos National Laboratory. The method not only could successfully identify the presence of damage but also has potential to localize it.

Spatial-Temporal Adaptive Optimal Allocation of Archival Tasks

“…Ideal archive allocation system [9] will allocate simple tasks such as archival translation tasks to the unskilled and difficult ones to the skilled. The increasing accumulated data on the system users [10,11,12] and the archival tasks make it possible to improve this allocation process.…”

Structural damage diagnosis under varying environmental conditions with very limited measurements

“…Furthermore, handling environmental variability is extremely difficult with modal based approaches. Several powerful signal processing techniques based on time series (Makki Alamdari et al, 2017; Tee, 2018), time–frequency analysis (An et al, 2013; An and Ou, 2012; Feng et al, 2013; Prawin and Rao, 2018; Rao and Lakshmi, 2015), multivariate analysis (Chao et al, 2014; Deraemaeker and Worden, 2018; Feng et al, 2013; Jolliffe and Cadima, 2016; Lakshmi et al, 2017; Lakshmi and Rama Mohan Rao, 2014; Prawin et al, 2015, 2016, 2018; Prawin and Rao, 2019; Rao et al, 2012; Yan et al, 2005; Yan and Golinval, 2006; Yang et al, 2016; Worden et al, 2002), combination of time series analysis, neural networks and the statistical inference technique (Sohn et al, 2002), switching state space autoregressive models (Liu et al, 2019) and so on are now available for damage identification which can handle operational and environmental variabilities. Apart from the above, four machine learning algorithms based on the auto-associative neural network, factor analysis, Mahalanobis distance, and singular value decomposition (SVD) have been investigated by Figueiredo et al (2011) for damage diagnosis under operational and environmental variabilities.…”

Symbolic dynamics time series analysis for assessment of barely visible indentation damage in composite sandwich structures based on guided waves

“…Over the last three decades, Lamb waves have been used to detect various types of damage in composites and metallic structures, including corrosion, fatigue cracking, delamination, fiber breakage, and debonding. 5,6,[14][15][16][17][18][19][20][21] Recently, symbolic dynamics (SD) have been introduced to characterize time series responses in many applications including SHM, [22][23][24] online fatigue damage monitoring using wave based signals, 25 underwater object detection, 26 anomaly detection in electronic systems, 27 weather forecasting, 28 and currency exchange monitoring. 29 In a structural system, SD evaluates the progress of variation in the system dynamics to monitor any gradual change that indicates growing structural damage.…”

“…30 The application of this technique does not depend on the nature of the system, being deterministic or stochastic, linear or nonlinear. [22][23][24] Rather, it centers on transforming time series records into symbol sequences, reducing computational time while conserving the main characteristics. [22][23][24] Moreover, it has been demonstrated that this technique enhances the signal-to-noise ratio.…”

“…[22][23][24] Rather, it centers on transforming time series records into symbol sequences, reducing computational time while conserving the main characteristics. [22][23][24] Moreover, it has been demonstrated that this technique enhances the signal-to-noise ratio. 31 Minimum work has been done on the application of symbolic time series analysis (STSA) to ultrasonic waves; probably the only work reported in the literature was by Gupta et al 25 They made use of STSA for early detection and online monitoring of fatigue damage in polycrystalline alloys (7075-T6 aluminum) subjected to low-and high-cycle fatigue.…”