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)

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

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

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

INE/AUTC 10.0

Fuzzy finite element model updating of a laboratory wind turbine blade for structural modification detection

Peer reviewedPublisher PD

Detection and localization of trailing edge disbond in a large wind turbine blade

Peer reviewedPreprin

Damage monitoring in sandwich beams by modal parameter shifts: a comparative study of burst random and sine dwell vibration testing

This paper presents an experimental study on the effects of multi-site damage on the vibration response of honeycomb sandwich beams, damaged by two different ways i.e., impact damage and core-only damage simulating damage due to bird or stone impact or due to mishandling during assembly and maintenance. The variation of the modal parameters with different levels of impact energy and density of damage is studied. Vibration tests have been carried out with both burst random and sine dwell testing in order to evaluate the damping estimation efficiency of these methods in the presence of damage. Sine dwell testing is done in both up and down frequency directions in order to detect structural non-linearities. Results show that damping ratio is a more sensitive parameter for damage detection than the natural frequency. Design of experiments (DOE) highlighted density of damage as the factor having a more significant effect on the modal parameters and also proved that sine dwell testing is more suitable for damping estimation in the presence of damage as compared to burst random testing

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

Physico-electrochemical Characterization of Pluripotent Stem Cells during Self-Renewal or Differentiation by a Multi-modal Monitoring System.

Monitoring pluripotent stem cell behaviors (self-renewal and differentiation to specific lineages/phenotypes) is critical for a fundamental understanding of stem cell biology and their translational applications. In this study, a multi-modal stem cell monitoring system was developed to quantitatively characterize physico-electrochemical changes of the cells in real time, in relation to cellular activities during self-renewal or lineage-specific differentiation, in a non-destructive, label-free manner. The system was validated by measuring physical (mass) and electrochemical (impedance) changes in human induced pluripotent stem cells undergoing self-renewal, or subjected to mesendodermal or ectodermal differentiation, and correlating them to morphological (size, shape) and biochemical changes (gene/protein expression). An equivalent circuit model was used to further dissect the electrochemical (resistive and capacitive) contributions of distinctive cellular features. Overall, the combination of the physico-electrochemical measurements and electrical circuit modeling collectively offers a means to longitudinally quantify the states of stem cell self-renewal and differentiation

GPS monitoring of a steel box girder viaduct

Structural performance monitoring of bridges has increased as major infrastructure ages and is required to sustain loads that are significantly greater than those predicted during design. Structural stiffness and/or mass distribution can change over the lifespan of a bridge structure. Resulting changes in profile or resonant frequency provide key indicators of change, and may identify structural defects. Field tests using GPS for monitoring relatively small deformations were carried out on a steel box girder viaduct bridge in the UK. The configuration consisted of five GPS receivers located at key locations on the viaduct and two reference GPS receivers. GPS data was collected at either 10 Hz or 20 Hz and post-processed using proprietary software, along with appropriate filtering and spectral analysis. Three main frequencies were clearly detected by the GPS in the vertical component. A previously reported frequency of approximately 0.56 Hz was identified along with two other frequencies. The peak vertical deflections lie in the range of ± 50 mm, while lateral and longitudinal deflections of much smaller magnitude - in the order of a few mm - are also measured. The use of GPS leads to readily obtained and useful engineering data for continued monitoring of structures