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

INE/AUTC 10.0

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)

Low weight additive manufacturing FBG accelerometer: design, characterization and testing

Structural Health Monitoring is considered the process of damage detection and structural characterization by any type of on-board sensors. Fibre Bragg Gratings (FBG) are increasing their popularity due to their many advantages like easy multiplexing, negligible weight and size, high sensitivity, inert to electromagnetic fields, etc. FBGs allow obtaining directly strain and temperature, and other magnitudes can also be measured by the adaptation of the Bragg condition. In particular, the acceleration is of special importance for dynamic analysis. In this work, a low weight accelerometer has been developed using a FBG. It consists in a hexagonal lattice hollow cylinder designed with a resonance frequency above 500 Hz. A Finite Element Model (FEM) was used to analyse dynamic behaviour of the sensor. Then, it was modelled in a CAD software and exported to additive manufacturing machines. Finally, a characterization test campaign was carried out obtaining a sensitivity of 19.65 pm/g. As a case study, this paper presents the experimental modal analysis of the wing of an Unmanned Aerial Vehicle. The measurements from piezoelectric, MEMS accelerometers, embedded FBGs sensors and the developed FBG accelerometer are compared.Ministerio de Economía y Competitividad BIA2013-43085-P y BIA2016-75042-C2-1-

A multisensing setup for the intelligent tire monitoring

The present paper offers the chance to experimentally measure, for the first time, the internal tire strain by optical fiber sensors during the tire rolling in real operating conditions. The phenomena that take place during the tire rolling are in fact far from being completely understood. Despite several models available in the technical literature, there is not a correspondently large set of experimental observations. The paper includes the detailed description of the new multi-sensing technology for an ongoing vehicle measurement, which the research group has developed in the context of the project OPTYRE. The experimental apparatus is mainly based on the use of optical fibers with embedded Fiber Bragg Gratings sensors for the acquisition of the circumferential tire strain. Other sensors are also installed on the tire, such as a phonic wheel, a uniaxial accelerometer, and a dynamic temperature sensor. The acquired information is used as input variables in dedicated algorithms that allow the identification of key parameters, such as the dynamic contact patch, instantaneous dissipation and instantaneous grip. The OPTYRE project brings a contribution into the field of experimental grip monitoring of wheeled vehicles, with implications both on passive and active safety characteristics of cars and motorbikes

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

Structural Health Monitoring of Large Structures Using Acoustic Emission-Case Histories

Acoustic emission (AE) techniques have successfully been used for assuring the structural integrity of large rocket motorcases since 1963 [...

Structural health monitoring of a masonry viaduct with Fibre Bragg Grating sensors

The Marsh Lane viaduct is a masonry railway bridge constructed during the 19th century nearby Leeds Central Railway Station. The bridge appears significantly damaged due to the increase of the operational train loads over the last decades and due to environmental effects. Due to this degradation, extensive repair was conducted in 2015. After this repair work, an extensive fibre optic sensor network was installed below three spans of the bridge to monitor surface strains at 68 locations on the underside of the arch spans. The paper compares data collected from two monitoring periods, 16 months apart. Combining statistical analysis and signal processing techniques, the results show that local damage, as well as change in the global dynamic behaviour of the structure over time, can be effectively detected with the use of Fibre Bragg Grating sensors.This work is being funded by the Lloyd’s Register Foundation, EPSRC and Innovate UK through the Data-Centric Engineering programme of the Alan Turing Institute and through the Cambridge Centre for Smart Infrastructure and Construction (CSIC). Funding for the monitoring installation was provided by EPSRC under the Ref. EP/N021614/1 grant and by Innovate UK under the Ref. 920035 grant

Monitoring bridge degradation using dynamic strain, acoustic emission and environmental data

This paper studies the long term structural behaviour of a Victorian railway viaduct under train loading and temperature variation. A multi-sensing, self-sustaining and remotely controlled data acquisition system combines fibre Bragg grating strain sensors with acoustic emission sensors for the study of both global dynamic deformation and local masonry deterioration. A statistical analysis of fibre Bragg grating signals reveals regions with permanent change in the dynamic deformation of the bridge over the last two years, whereas in other locations the deformation follows a seasonal cyclic pattern. In order to decouple changes in structural behaviour due to real mechanical damage from normal seasonal effect, the paper studies the ambient temperature effect on the dynamic deformation of the bridge, showing a clear linear dependence. In particular, when temperature increases, the dynamic strain due to train loading decreases uniformly in the longitudinal direction. In the transverse direction, where the thermal expansion is not constrained, the decrease is smaller. Decoupling damage from normal seasonal effect is of critical importance for the development of reliable early warning structural alert systems for infrastructure networks. The paper further studies local masonry deterioration at four critical location by combining data from the two sensing technologies: fibre optic and acoustic emission sensors.This work is being funded by the Lloyd’s Register Foundation, EPSRC and Innovate UK through the Data-Centric Engineering programme of the Alan Turing Institute and through the Cambridge Centre for Smart Infrastructure and Construction. Funding for the monitoring installation was provided by EPSRC under the Ref. EP/N021614/1 grant and by Innovate UK under the Ref. 920035 grant

On the performance of a cointegration-based approach for novelty detection in realistic fatigue crack growth scenarios

Confounding influences, such as operational and environmental variations, represent a limitation to the implementation of Structural Health Monitoring (SHM) systems in real structures, potentially leading to damage misclassifications. In this framework, this study considers cointegration as a state of the art method for data normalisation in fatigue crack propagation scenarios, where monitoring is performed by a distributed network of strain sensors. Specifically, the work is aimed at demonstrating the effectiveness of cointegration on real engineering data in a new context, where the damage is continuously growing. Cointegration is applied at first in a controlled scenario consisting of a numerical strain simulation by means of a finite element model, modified in order to take realistic temperature fluctuations and sensor noise into account. Afterwards, detrending and anomaly detection performances are verified in two different experimental programmes on realistic aeronautical structures subjected to fatigue crack growth, including a full-scale fatigue test on a helicopter tail boom. Strain measurements are taken from a network of Fibre Bragg Grating (FBG) sensors, known to be extremely sensitive to temperature variations, hence delivering challenging scenarios for cointegration testing. Results are shown to be in good agreement with the experimental evidence, with the cointegration algorithm capable of detecting the onset of damage propagation within a 4 mm increment from a baseline condition