Damage localization based on symbolic time series analysis

Copyright © 2014 John Wiley & Sons, Ltd. The objective of this paper is to localize damage in a single or multiple state at early stages of development on the basis of the principles of symbolic dynamics. Symbolic time series analysis (STSA) of noise-contaminated responses is used for feature extraction to detect and localize a gradually evolving deterioration in the structure according to the changes in the statistical behaviour of symbol sequences. Basically, in STSA, statistical features of the symbol sequence can be used to describe the dynamic status of the system. Symbolic dynamics has some useful characteristics making it highly demanded for implementation in real-time observation application such as SHM. First, it significantly reduces the dimension of information and provides information-rich representation of the underlying data. Second, symbolic dynamics and the set of statistical measures built upon it represent a solid framework to address the main challenges of the analysis of nonstationary time data. Finally, STSA often allows capturing the main features of the underlying system whilst alleviating the effects of harmful noise. The method presented in this paper consists of four primary steps: (i) acquisition of the time series data; (ii) creating the symbol space to produce symbol sequences on the basis of the wavelet transformed version of time series data; (iii) developing the symbol probability vectors to achieve anomaly measures; and (iv) localizing damage on the basis of any sudden variation in anomaly measure of different locations. The method was applied on a flexural beam and a 2-D planar truss bridge subjected to varying Gaussian excitation in presence of 2% white noise to examine the efficiency and limitations of the method. Simulation results under various damage conditions confi rmed the efficiency of the proposed approach for localization of gradually evolving deterioration in the structure; however, for the future work, the method needs to be verified by experimental data

Automated algorithm for impact force identification using cosine similarity searching

© 2018 A similarity searching technique is adopted to identify the impact force applied on a rectangular carbon fibre-epoxy honeycomb composite panel. The purpose of this study is to simultaneously identify both the location and magnitude of an unknown impact using the measured dynamic response collected by only a single piezoelectric sensor. The algorithm assumes that a set of impact forces are concurrently applied on a set of pre-defined locations. However, the magnitude of all the impact forces except one is considered to be zero. The impact force at all potential locations is then reconstructed through an l2-norm-based regularisation via two strategies: even-determined approach and under-determined approach. In an even-determined approach, the reconstruction process is performed independently for each pair of sensor and potential impact location. However, in an under-determined approach, the captured vibration signal is the superposition of the responses of the simultaneous ‘assumed’ impacts at the potential locations. Using either approach, a reconstructed impact force is obtained for each potential impact location. The reconstructed impact forces at spurious locations are expected to have zero magnitude as no impact has actually occurred at these locations. However, there might be some non-zero reconstructed impact forces at spurious locations. Therefore, it is worth designing an automated algorithm capable of detecting the most probable location. Cosine similarity searching is adopted to measure the intensity of the relationship between the reconstructed forces and an impact-like signal with various scale parameters. The largest value of cosine among all reconstructed forces corresponds to the most probable impact location. The results illustrate successful identification of the impact force location and magnitude for both even-determined and under-determined approaches

Model Updating for Loading Capacity Estimation of Concrete Structures using Ambient Vibration

This paper presents a model updating approach for determining the loading capacity of a concrete structure utilising measured ambient vibration responses. The proposed method uses Operational Modal Analysis (OMA) with the Enhanced Frequency Domain Decomposition (EFDD) technique to identify the natural frequencies and mode shapes of an experimental replica specimen of a Sydney Harbour Bridge concrete jack arch component. For vibration testing, the structure is excited with ambient vibration recordings from the actual Sydney Harbour Bridge using a vibro tactile transducer. The vibration responses of the structure are measured using an array of strategically placed accelerometers. A numerical model is developed and updated using the vibrational characteristics with the aim of estimating the load capacity of the structure. The results show that the proposed updating method using partitioning has great potential to be used for determining the loading capacity of a structure as part of a Structural Health Monitoring (SHM) system

Simulation of various damage scenarios using finite element modelling for structural health monitoring systems

© 2017 Taylor & Francis Group, London. Structural Health Monitoring (SHM) is a developing technology for asset management of structures including bridge assets. A crucial benefit of SHM is its ability to monitor the health status of structures using continuous measurements. As a key in SHM, the application of damage detection algorithms to assess the condition of a structure using vibration measurements can be enhanced by providing structural information under various damaged scenarios, which can be obtained from updated numerical models that realistically represent the in-situ structure. However, the dynamic characteristics of a structure are sensitive to uncertainties of various parameters, including material properties and boundary conditions, which require updating in the Finite Element (FE) model to ensure that the model replicates the actual structure. This study focuses on the development of an FE model for the accurate simulation of a jack arch replica structure of the Sydney Harbour Bridge. An experimental jack arch replica is produced to simulate various damage scenarios for laboratory testing. A matching FE model of the jack arch replica is generated and updated using Genetic Algorithm (GA) based on experimental measurements. Damage is simulated in the updated model and the results are validated using the experimental test results. The successful simulation of damage using updated FE models enables the generation of a large number of damage cases that can be trained into an SHM system to improve its damage detection capabilities

FRF Sensitivity-Based Damage Identification Using Linkage Modeling for Limited Sensor Arrays

© 2018 World Scientific Publishing Company. This paper presents a novel method to localize and quantify damage in a jack arch structure by introducing a linkage modeling technique to overcome issues caused by having limited sensors. The main strategy in the proposed Frequency Response Function (FRF)-based sensitivity model updating approach is to divide the specimen into partitions. The Young's modulus of each partition is then updated to detect stiffness reduction caused by damage. System Equivalent Reduction Expansion Process (SEREP) is used to reduce the full finite element (FE) model to a linkage model. The number of measured degrees of freedom (DOFs) is then expanded to the linkage model using the mass and stiffness matrices of the linkage model for the synthesis of interpolated FRFs. The FRF sensitivities are then formulated using the linkage model along with the interpolated FRFs to iteratively calculate the values of the updating parameters until convergence is achieved. The methodology and theory behind this procedure are discussed and verified using a numerical and experimental study. The successful implementation of this method has the potential to detect the location and severity of damage where sensor placement is limited

A multi-way data analysis approach for structural health monitoring of a cable-stayed bridge

© The Author(s) 2018. A large-scale cable-stayed bridge in the state of New South Wales, Australia, has been extensively instrumented with an array of accelerometer, strain gauge, and environmental sensors. The real-time continuous response of the bridge has been collected since July 2016. This study aims at condition assessment of this bridge by investigating three aspects of structural health monitoring including damage detection, damage localization, and damage severity assessment. A novel data analysis algorithm based on incremental multi-way data analysis is proposed to analyze the dynamic response of the bridge. This method applies incremental tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies. A total of 15 different damage scenarios were investigated; damage was physically simulated by locating stationary vehicles with different masses at various locations along the span of the bridge to change the condition of the bridge. The effect of damage on the fundamental frequency of the bridge was investigated and a maximum change of 4.4% between the intact and damage states was observed which corresponds to a small severity damage. Our extensive investigations illustrate that the proposed technique can provide reliable characterization of damage in this cable-stayed bridge in terms of detection, localization and assessment. The contribution of the work is threefold; first, an extensive structural health monitoring system was deployed on a cable-stayed bridge in operation; second, an incremental tensor analysis was proposed to analyze time series responses from multiple sensors for online damage identification; and finally, the robustness of the proposed method was validated using extensive field test data by considering various damage scenarios in the presence of environmental variabilities

Sex Bias in Pathogenesis of Autoimmune Neuroinflammation: Relevance for Dimethyl Fumarate Immunomodulatory/Anti-oxidant Action

In the present study, upon showing sexual dimorphism in dimethyl fumarate (DMF) efficacy to moderate the clinical severity of experimental autoimmune encephalomyelitis (EAE) in Dark Agouti rats, cellular and molecular substrate of this dimorphism was explored. In rats of both sexes, DMF administration from the day of immunization attenuated EAE severity, but this effect was more prominent in males leading to loss of the sexual dimorphism observed in vehicle-administered controls. Consistently, in male rats, DMF was more efficient in diminishing the number of CD4+ T lymphocytes infiltrating spinal cord (SC) and their reactivation, the number of IL-17+ T lymphocytes and particularly cellularity of their highly pathogenic IFN-gamma+GM-CSF+IL-17+ subset. This was linked with changes in SC CD11b+CD45+TCR alpha beta- microglia/proinflammatory monocyte progeny, substantiated in a more prominent increase in the frequency of anti-inflammatory phygocyting CD163+ cells and the cells expressing high surface levels of immunoregulatory CD83 molecule (associated with apoptotic cells phagocytosis and implicated in downregulation of CD4+ T lymphocyte reactivation) among CD11b+CD45+TCR alpha beta- cells in male rat SC. These changes were associated with greater increase in the nuclear factor (erythroid-derived 2)-like 2 expression in male rats administered with DMF. In accordance with the previous findings, DMF diminished reactive nitrogen and oxygen species generation and consistently, SC level of advanced oxidation protein products, to the greater extent in male rats. Overall, our study indicates sex-specificity in the sensitivity of DMF cellular and molecular targets and encourages sex-based clinical research to define significance of sex for action of therapeutic agents moderating autoimmune neuroinflammation-/oxidative stress-related nervous tissue damage

Wavelet transform-based strategy for identifying impact force on a composite panel

© Proceedings of the 26th International Congress on Sound and Vibration, ICSV 2019. All rights reserved. An algorithm based on wavelet analysis for automatically estimating the location and magnitude of impact forces exerted on a rectangular carbon fibre-epoxy honeycomb composite panel is developed. The technique employs a single piezoelectric sensor mounted distant from the impact zone and presumes that an impact is applied at one of several pre-established locations. Furthermore, it is presumed that the recorded vibration response is the superposition of the simultaneous 'assumed' impacts at these locations, with the aim of simultaneously identifying the actual impact location and force magnitude through an under-determined regularisation scheme. The algorithm aims to detect the most probable impact location amongst the spurious locations. Since a normal impact introduces a narrow-band time-localised event with high energy, the wavelet transform is an effective tool to locate this event, with the wavelet coefficient representing how closely correlated the wavelet is with the reconstructed forces. The larger the coefficient is in absolute value, the greater the similarity. As a case study, an under-determined problem with four potential impact locations is considered. The results demonstrate successful localisation and reconstruction of the impact force using both orthogonal and non-orthogonal wavelet

A spectral-based clustering for structural health monitoring of the Sydney Harbour Bridge

© 2016 Elsevier Ltd This paper presents the results of a large scale Structural Health Monitoring application on the Sydney Harbour Bridge in Australia. This bridge has many structural components, and our work focuses on a subset of 800 jack arches under the traffic lane 7. Our goal is to identify which of these jack arches (if any) respond differently to the traffic input, due to potential structural damages or instrumentation issues. We propose a novel non-model-based method to achieve this objective, using a spectrum-driven feature based on the Spectral Moments (SMs) from measured responses from the jack arches. SMs contain information from the entire frequency range, thus subtle differences between the normal signals and distorted ones could be identified. Our method then applies a modified k-means−− clustering algorithm to these features, followed by a selection mechanism on the clustering results to identify jack arches with abnormal responses. We performed an extensive evaluation of the proposed method using real data from the bridge. This evaluation included a control component, where the approach successfully detected jack arches with already known damage or issues. It also included a test component, which applied the method to a large set of nodes over a month of data to detect any potential anomaly. The detected anomalies turned out to have indeed system issues after further investigations