Advanced Sensors for Real-Time Monitoring Applications

It is impossible to imagine the modern world without sensors, or without real-time information about almost everything—from local temperature to material composition and health parameters. We sense, measure, and process data and act accordingly all the time. In fact, real-time monitoring and information is key to a successful business, an assistant in life-saving decisions that healthcare professionals make, and a tool in research that could revolutionize the future. To ensure that sensors address the rapidly developing needs of various areas of our lives and activities, scientists, researchers, manufacturers, and end-users have established an efficient dialogue so that the newest technological achievements in all aspects of real-time sensing can be implemented for the benefit of the wider community. This book documents some of the results of such a dialogue and reports on advances in sensors and sensor systems for existing and emerging real-time monitoring applications

Reconstruction of sleeper displacements from measured accelerations for model-based condition monitoring of railway crossing panels

Railway switches and crossings (S&C, turnouts) connect different track sections and create a railway network by allowing trains to change tracks. This functionality comes at a cost as the load-inducing rail discontinuities in the switch and crossing panels cause much larger degradation rates for S&C compared to regular plain line tracks. The high degradation rates make remote condition monitoring an interesting prospect for infrastructure managers to optimise maintenance and ensure safe operations. To this end, this paper addresses the development of tailored signal processing tools for condition monitoring using embedded accelerometers in crossing panels. Multibody simulations of the dynamic train–track interaction are used to aid the interpretation of the measured signals in a first step towards building a model-based condition monitoring system. An analysis is performed using sleeper acceleration measurement data generated by 100 000 train passages in eight crossing panels. Based on the given data, a novel frequency-domain displacement reconstruction method is developed and the robustness of the method with respect to encountered operational variability of the measured data is demonstrated. The separation of the track response into quasi-static and dynamic domains based on deformation wavelength regions is proposed as a promising strategy to observe the ballast condition and the crossing geometry condition, respectively

Local track irregularity identification based on multi-sensor time-frequency features of high-speed railway bridge accelerations

Shortwave track diseases are generally reflected in the form of local track irregularity. Such diseases will greatly impact the train-track-bridge interaction (TTBI) dynamic system, seriously affecting train safety. Therefore, a method is proposed to detect and localize local track irregularities based on multis-sensor time-frequency features of high-speed railway bridge accelerations. Continuous wavelet transform (CWT) is used to analyze the multi-sensor accelerations of railway bridges. Moreover, time-frequency features based on the sum of wavelet coefficients are proposed, considering the influence of the distance from the measurement points to the local irregularity on the recognition accuracy. Then, the multi-domain features are utilized to recognize deteriorated railway locations. A simply-supported high-speed railway bridge traversed by a railway train is adopted as a numerical simulation. Comparative studies are conducted to investigate the influence of vehicle speeds and the location of local track irregularity on the algorithm. Numerical simulation results show that the proposed algorithm can detect and locate local track irregularity accurately and is robust to vehicle speeds

Vision-based systems for structural deformation measurement: case studies

This is the author accepted manuscript. The final version is available from Thomas Telford (ICE Publishing) via the DOI in this record.Vision-based systems offer a promising way for displacement measurement and receive increased attention in civil structural monitoring. However, the working performance of vision-based systems, especially the measurement accuracy and the robustness to different field conditions is not fully understood. This study reports three cases studies of vision-based monitoring tests including one in a laboratory, one on a short-span bridge and one on a long-span bridge. The tracking accuracy is quantified in laboratory conditions in the range of 0.02 pixel to 0.20 pixel depending on the target patterns as well as the tracking method selected. The measurement performance under several field challenges are investigated including long-range measurement (e.g. camera-to-target distance at 710 m), low-contrast target patterns, changes of target patterns and changes in lighting conditions. Three representative tracking methods for the video processing, i.e. correlation-based template matching, Lucas Kanade (LK) optical flow estimation and scale-invariant feature transform (SIFT) were used for analysis, indicating their advantages and shortcomings for field measurement. One of the main observations in field application is that changes in lighting conditions might cause some low-frequency measurement error that could be misunderstood without the prior knowledge about structural loading conditions

Vibration-based monitoring of civil infrastructure: challenges and successes

Author's manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s13349-011-0009-5© Springer-Verlag 2011Co -published with International Society for Structural Health Monitoring of Intelligent InfrastructureStructural health monitoring (SHM) is a relatively new paradigm for civil infrastructure stakeholders including operators, consultants and contractors which has in the last two decades witnessed an acceleration of academic and applied research in related areas such as sensing technology, system identification, data mining and condition assessment. SHM has a wide range of applications including, but not limited to, diagnostic and prognostic capabilities. However, when it comes to practical applications, stakeholders usually need answers to basic and pragmatic questions about in-service performance, maintenance and management of a structure which the technological advances are slow to address. Typical among the mismatch of expectation and capability is the topic of vibration-based monitoring (VBM), which is a subset of SHM. On the one hand there is abundant reporting of exercises using vibration data to locate damage in highly controlled laboratory conditions or in numerical simulations, while the real test of a reliable and cost effective technology is operation on a commercial basis. Such commercial applications are hard to identify, with the vast majority of implementations dealing with data collection and checking against parameter limits. In addition there persists an unhelpful association between VBM and 'damage detection' among some civil infrastructure stakeholders in UK and North America, due to unsuccessful transfer of technology from the laboratory to the field, and this has resulted in unhealthy industry scepticism which hinders acceptance of successful technologies. Hence the purpose of this paper is showcase successful VBM applications and to make the case that VBM does provide valuable information in real world applications when used appropriately and without unrealistic expectations. © 2011 Springer-Verlag

Characterization of Road Condition with Data Mining Based on Measured Kinematic Vehicle Parameters

This work aims at classifying the road condition with data mining methods using simple acceleration sensors and gyroscopes installed in vehicles. Two classifiers are developed with a support vector machine (SVM) to distinguish between different types of road surfaces, such as asphalt and concrete, and obstacles, such as potholes or railway crossings. From the sensor signals, frequency-based features are extracted, evaluated automatically with MANOVA. The selected features and their meaning to predict the classes are discussed. The best features are used for designing the classifiers. Finally, the methods, which are developed and applied in this work, are implemented in a Matlab toolbox with a graphical user interface. The toolbox visualizes the classification results on maps, thus enabling manual verification of the results. The accuracy of the cross-validation of classifying obstacles yields 81.0% on average and of classifying road material 96.1% on average. The results are discussed on a comprehensive exemplary data set

Investigation of Computer Vision Concepts and Methods for Structural Health Monitoring and Identification Applications

This study presents a comprehensive investigation of methods and technologies for developing a computer vision-based framework for Structural Health Monitoring (SHM) and Structural Identification (St-Id) for civil infrastructure systems, with particular emphasis on various types of bridges. SHM is implemented on various structures over the last two decades, yet, there are some issues such as considerable cost, field implementation time and excessive labor needs for the instrumentation of sensors, cable wiring work and possible interruptions during implementation. These issues make it only viable when major investments for SHM are warranted for decision making. For other cases, there needs to be a practical and effective solution, which computer-vision based framework can be a viable alternative. Computer vision based SHM has been explored over the last decade. Unlike most of the vision-based structural identification studies and practices, which focus either on structural input (vehicle location) estimation or on structural output (structural displacement and strain responses) estimation, the proposed framework combines the vision-based structural input and the structural output from non-contact sensors to overcome the limitations given above. First, this study develops a series of computer vision-based displacement measurement methods for structural response (structural output) monitoring which can be applied to different infrastructures such as grandstands, stadiums, towers, footbridges, small/medium span concrete bridges, railway bridges, and long span bridges, and under different loading cases such as human crowd, pedestrians, wind, vehicle, etc. Structural behavior, modal properties, load carrying capacities, structural serviceability and performance are investigated using vision-based methods and validated by comparing with conventional SHM approaches. In this study, some of the most famous landmark structures such as long span bridges are utilized as case studies. This study also investigated the serviceability status of structures by using computer vision-based methods. Subsequently, issues and considerations for computer vision-based measurement in field application are discussed and recommendations are provided for better results. This study also proposes a robust vision-based method for displacement measurement using spatio-temporal context learning and Taylor approximation to overcome the difficulties of vision-based monitoring under adverse environmental factors such as fog and illumination change. In addition, it is shown that the external load distribution on structures (structural input) can be estimated by using visual tracking, and afterward load rating of a bridge can be determined by using the load distribution factors extracted from computer vision-based methods. By combining the structural input and output results, the unit influence line (UIL) of structures are extracted during daily traffic just using cameras from which the external loads can be estimated by using just cameras and extracted UIL. Finally, the condition assessment at global structural level can be achieved using the structural input and output, both obtained from computer vision approaches, would give a normalized response irrespective of the type and/or load configurations of the vehicles or human loads

Developing TRACKER - Portable Monitoring System using Kalman Filtering to Track Rotational Movement of Bridges

The combined effects of flooding and scour are the primary causes of bridge failure over flowing water. Improvements in structural health monitoring and inertial sensors have led to the development of advanced monitoring systems that can provide bridge owners with detailed information on the performance of the structure and allow informed decisions to be made about time-critical safety issues following a storm event. However, such systems remain prohibitively expensive for the majority of smaller structures which make up the wider transport network. This thesis details the development of a robust, portable data acquisition logger (TRACK ER), which can be used to target vulnerable infrastructure during a storm event to increase the resilience of the wider transport network. TRACKER uses condition monitoring, recording quasi-static and dynamic deformations, to track the performance of a bridge under the combined effects of storm loading. A benefit of this method is that it requires no direct input force or prior knowledge of the bridge model. Traditionally, tiltmeters or accelerometers are used to measure rotation for structural health monitoring purposes but such sensors can struggle to isolate rotation from translational acceleration if the structure is linearly accelerating. Gyroscopes offer improved rotational measurement capabilities but gyroscope measurements are known to drift over time as a result of the iterative process of converting rate gyroscope data. This thesis will explore gyroscopes as a complementary sensor to accelerometers and introduce a Kalman filter that combines both inertial sensors measurement data to obtain optimised rotation data. To improve the performance of the Kalman filter, the filter is adapted to automatically update the process and noise measurement values. TRACKER, a robust, portable data acquisition logger, was developed and equipped with inertial sensors to provide a stand-alone system that can be rapidly deployed to target vulnerable infrastructure. Verification of the new logger was performed under controlled laboratory conditions to prove the validity of the new logger. The rotational data showed good agreement with rotational measurements obtained from an industry gold-standard vision-based measurement system. TRACKER was deployed on a variety of in-service bridges using different loading scenarios to demonstrate the ability of the new logging system, including loading from ambient weather conditions. TRACKER successfully tracked the performance of the structures, proving the ability of the logger to track the quasi-static and dynamic deformations of a structure during loading from traffic and environmental conditions