Wireless sensor integration for bridge model health monitoring

An integrated hardware and software system for a scalable wireless sensor network (WSN) is designed and developed for structural health monitoring. An extension sensor board is designed, developed, and calibrated to meet the requirements for structural vibration monitoring and modal identification. The extension sensor board has 3 axes of accelerometers in three directions and a temperature sensor. Software components have been implemented within the TinyOS operating system to provide a flexible software platform and scalable performance for structural health monitoring applications. The prototype WSN is deployed on a reduced-scale bridge model with 3 nodes in a single-hop network for performing dynamic monitoring civil engineering structures. Two output-only time-domain system identification methods are employed namely, the Frequency Domain Decomposition (FDD) method and the Natural Excitation Technique (NExT) combined with the Eigensystem Realization Algorithm (ERA). Testing results show that the WSN provides accurate vibration data for identifying vibration modes of a bridge

Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

INE/AUTC 10.0

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Design of Wireless Sensor Nodes for Structural Health Monitoring applications

Enabling low-cost distributed monitoring, wireless sensor networks represents an interesting solution for the implementation of structural health monitoring systems. This work deals with the design of wireless sensor networks for health monitoring of civil structures, specifically focusing on node design in relation to the requirements of different structural monitoring application classes. Design problems are analysed with specific reference to a large-scale experimental setup (the long-term structural monitoring of the Basilica S. Maria di Collemaggio, L’Aquila, Italy). Main limitations emerged are highlighted, and adopted solution strategies are outlined, both in the case of commercial sensing platform and of full custom solutions

Performance monitoring of the Geumdang Bridge using a dense network of high-resolution wireless sensors

As researchers continue to explore wireless sensors for use in structural monitoring systems, validation of field performance must be done using actual civil structures. In this study, a network of low-cost wireless sensors was installed in the Geumdang Bridge, Korea to monitor the bridge response to truck loading. Such installations allow researchers to quantify the accuracy and robustness of wireless monitoring systems within the complex environment encountered in the field. In total, 14 wireless sensors were installed in the concrete box girder span of the Geumdang Bridge to record acceleration responses to forced vibrations introduced by a calibrated truck. In order to enhance the resolution of the capacitive accelerometers interfaced to the wireless sensors, a signal conditioning circuit that amplifies and filters low-level accelerometer outputs is proposed. The performance of the complete wireless monitoring system is compared to a commercial tethered monitoring system that was installed in parallel. The performance of the wireless monitoring system is shown to be comparable to that of the tethered counterpart. Computational resources (e.g. microcontrollers) coupled with each wireless sensor allow the sensor to estimate modal parameters of the bridge such as modal frequencies and operational displacement shapes. This form of distributed processing of measurement data by a network of wireless sensors represents a new data management paradigm associated with wireless structural monitoring.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/49011/2/sms6_6_008.pd

Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Offshore Wind has become the most profitable renewable energy source due to the remarkable development it has experienced in Europe over the last decade. In this paper, a review of Structural Health Monitoring Systems (SHMS) for offshore wind turbines (OWT) has been carried out considering the topic as a Statistical Pattern Recognition problem. Therefore, each one of the stages of this paradigm has been reviewed focusing on OWT application. These stages are: Operational Evaluation; Data Acquisition, Normalization and Cleansing; Feature Extraction and Information Condensation; and Statistical Model Development. It is expected that optimizing each stage, SHMS can contribute to the development of efficient Condition-Based Maintenance Strategies. Optimizing this strategy will help reduce labor costs of OWTs׳ inspection, avoid unnecessary maintenance, identify design weaknesses before failure, improve the availability of power production while preventing wind turbines׳ overloading, therefore, maximizing the investments׳ return. In the forthcoming years, a growing interest in SHM technologies for OWT is expected, enhancing the potential of offshore wind farm deployments further offshore. Increasing efficiency in operational management will contribute towards achieving UK׳s 2020 and 2050 targets, through ultimately reducing the Levelised Cost of Energy (LCOE)