24,358 research outputs found

    Experimental investigation on the use of multiple very low-cost inertial-based devices for comfort assessment and rail track monitoring

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    The periodic rail track inspection is mandatory to ensure ride comfort and operational safety. However, conventional monitoring technologies have high costs, stimulating research on low-cost alternatives. In this regard, this paper presents the first experimental results on the use of multiple very low-cost sensors aboard trains for vibration monitoring, proposing a collective approach to provide more accurate and robust results. Nine devices comprising commercial-grade inertial sensors were tested in different distributions aboard a high-speed track recording train. Frequency weighted accelerations were calculated in accordance with ISO 2631 standard as comfort and indirect track quality index. As expected, vertical and lateral results were correlated with, respectively, track longitudinal level (range D1, maximum correlation coefficient of 0.86) and alignment (range D2, maximum correlation coefficient of 0.60), with numerically similar results when considering the fused signal. The collective approach's potential was proven as a result of the noise reduction and the discrepant sensor identification

    Bridge damage detection based on vibration data: past and new developments

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    Overtime, bridge condition declines due to a number of degradation processes such as creep, corrosion, and cyclic loading, among others. Traditionally, vibration-based damage detection techniques in bridges have focused on monitoring changes to modal parameters. These techniques can often suffer to their sensitivity to changes in environmental and operational conditions, mistaking them as structural damage. Recent research has seen the emergence of more advanced computational techniques that not only allow the assessment of noisier and more complex data but also allow research to veer away from monitoring changes in modal parameters alone. This paper presents a review of the current state-of-the-art developments in vibration-based damage detection in small to medium span bridges with particular focus on the utilization of advanced computational methods that avoid traditional damage detection pitfalls. A case study based on the S101 bridge is also presented to test the damage sensitivity to a chosen methodology.Peer ReviewedPostprint (published version

    Analysis of selected acceleration signals measurements obtained during supervised service conditions – study of hitherto approach

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    The subject matter of the paper is an analysis of chosen results of acceleration signals measurements obtained from the prototype of the Rail Vehicle and Rail Track Monitoring System. This prototype of the monitoring system measures acceleration signals on designated elements of the electric multiple unit (EMU). These elements comprise components such as: bogie frames, wheels and bodies of railway vehicles. The analysis was prepared on the basis of rail vehicle journeys on sample sections of the Polish National Railways (PKP Polskie Linie Kolejowe S.A.) network. The products of measurements were converted to values of specific diagnostic parameters (statistical parameters), e.g. an amplitude (zero-peak), a root mean square, a kurtosis coefficient, an interquartile range. Comparing the values of diagnostic parameters with their permissible values allows the monitoring of distinctive dynamic behaviors of rail vehicles and track condition, as well as the temperature of the bearings of rail vehicle wheelsets. It also allows the determining of the condition of rail vehicle structure. The permissible values of certain diagnostics parameters could not be obtained from the literature. Therefore, this paper in part presents a way of obtaining these permissible values. The main intention of the analysis described here is to determine the usability of various diagnostic parameters and to identify the course of further research related to condition monitoring and diagnostics of rail vehicles and tracks

    Artificial Intelligence for Damage Detection in Automotive Composite Parts: A Use Case

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    The detection and evaluation of damage in composite materials components is one of the main concerns for automotive engineers. It is acknowledged that defects appeared in the manufacturing stage or due to the impact and/or fatigue loads can develop along the vehicle riding. To avoid an unexpected failure of structural components, engineers ask for cheap methodologies assessing the health state of composite parts by means of continuous monitoring. Non Destructive Technique (NDT) for the damage assessment of composite structures are nowadays common and accurate, but an on-line monitoring requires properties as low cost, small size and low power that do not belong to common NDT. The presence of a damage in composite materials, either due to fatigue cycling or low-energy impact, leads to progressive degradation of elastic moduli and strengths. Since there is a well-known relationship between the elastic modulus reduction and the amount of damage, the stiffness degradation can be used for the scope of detecting the position and the amount of damage that has taken place. Relying on these concepts, a novel strain-based damage sensing procedure is here proposed, that can identify damages in composite structures by processing strain measures from a distributed sensors network. To achieve this result a combined Machine Learning pipeline, composed by Principal Component Analysis (PCA) and One Class Support Vector Machine (OC-SVM) is proposed. First, PCA learns a linear transformation on the undamaged measurements to reduce the data dimensionality; secondly, OC-SVM trained to detect anomalies in the projected components. A cross-validation procedure is used to find the optimal pipeline configuration. The methodology is virtually tested on a carbon fiber suspension. The results suggest dropping the first components of the PCA to feed the classifier. In addition, results show the capability of the algorithm to detect anomalies in the component strain response

    Accurate deformation monitoring on bridge structures using a cost-effective sensing system combined with a camera and accelerometers

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    This is the author accepted manuscript. The final version is available from American Society of Civil Engineers via the DOI in this record.Information on deformation is critical for bridge condition evaluation but accurate characterisation, usually via discrete displacement measurements, remains a challenging task. Vision-based systems are promising tools, possessing advantages of easy installation, low cost and adequate resolution in time and frequency domains. However, vision-based monitoring faces several field challenges and might fail to achieve the required level of working performance in some real-world test conditions e.g.involving low-contrast patterns and mounting instability of optical sensors. To make the best use of the potential of vision-based systems, a mixed sensing system consisting of a consumer-grade camera and an accelerometer is proposed in this study for accurate displacement measurement. The system considers automatic compensation of camera shake and involves autonomous data fusion process for noise reduction. The proposed system is demonstrated through a field monitoring test on a short-span railway bridge and is validated to offer higher accuracy and wider frequency range than using a camera alone. Displacement data by the mixed system are demonstrated to be viable for estimating bridge influence line, indicating the potential for bridge condition assessment

    System identification and structural health monitoring of bridge structures

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    This research study addresses two issues for the identification of structural characteristics of civil infrastructure systems. The first one is related to the problem of dynamic system identification, by means of experimental and operational modal analysis, applied to a large variety of bridge structures. Based on time and frequency domain techniques and mainly with output-only acceleration, velocity or strain data, modal parameters have been estimated for suspension bridges, masonry arch bridges, concrete arch and continuous bridges, reticular and box girder steel bridges. After giving an in-depth overview of standard and advanced stochastic methods, differences of the existing approaches in their performances are highlighted during system identification on the different kinds of civil infrastructures. The evaluation of their performance is accompanied by easy and hard determinable cases, which gave good results only after performing advanced clustering analysis. Eventually, real-time vibration-based structural health monitoring algorithms are presented during their performance in structural damage detection by statistical models. The second issue is the noise-free estimation of high order displacements taking place on suspension bridges. Once provided a comprehensive treatment of displacement and acceleration data fusion for dynamic systems by defining the Kalman filter algorithm, the combination of these two kinds of measurements is achieved, improving the deformations observed. Thus, an exhaustive analysis of smoothed displacement data on a suspension bridge is presented. The successful tests were subsequently used to define the non-collocated sensor monitoring problem with the application on simplified model
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