150 research outputs found

    Applicability of machine learning approaches for structural damage detection of offshore wind jacket structures based on low resolution data

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    Structural damage in offshore wind jacket support structures are relatively unlikely due to the precautions taken in design but it could imply dramatic consequences if undetected. This work explores the possibilities of damage detection when using low resolution data, which are available with lower costs compared to dedicated high-resolution structural health monitoring. Machine learning approaches showed to be generally feasible for detecting a structural damage based on SCADA data collected in a simulation environment. Focus is here given to investigate model uncertainties, to assess the applicability of machine learning approaches for reality. Two jacket models are utilised representing the as-designed and the as-installed system, respectively. Extensive semi-coupled simulations representing different operating load cases are conducted to generate a database of low-resolution signals serving the machine learning training and testing. The analysis shows the challenges of classification approaches, i.e. supervised learning aiming to separate healthy and damage status, in coping with the uncertainty in system dynamics. Contrarily, an unsupervised novelty detection approach shows promising results when trained with data from both, the as-designed and the as-installed system. The findings highlight the importance of investigating model uncertainties and careful selection of training data

    Involvement of XRCC1 and DNA Ligase III Gene Products in DNA Base Excision Repair

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    DNA ligase III and the essential protein XRCC1 are present at greatly reduced levels in the xrcc1 mutant CHO cell line EM-C11. Cell-free extracts prepared from these cells were used to examine the role of the XRCC1 gene product in DNA base excision repair in vitro. EM-C11 cell extract was partially defective in ligation of base excision repair patches, in comparison to wild type CHO-9 extracts. Of the two branches of the base excision repair pathway, only the single nucleotide insertion pathway was affected; no ligation defect was observed in the proliferating cell nuclear antigen-dependent pathway. Full complementation of the ligation defect in EM-C11 extracts was achieved by addition to the repair reaction of recombinant human DNA ligase III but not by XRCC1. This is consistent with the notion that XRCC1 acts as an important stabilizing factor of DNA ligase III. These data demonstrate for the first time that xrcc1 mutant cells are partially defective in ligation of base excision repair patches and that the defect is specific to the polymerase beta-dependent single nucleotide insertion pathway

    Reliability, availability, maintainability data review for the identification of trends in offshore wind energy applications

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    This work presents a comprehensive review and discussion of the identification of critical components of the currently installed and next generation of offshore wind turbines. A systematic review on the reliability, availability, and maintainability data of both offshore and onshore wind turbines is initially performed, collecting the results from 24 initiatives, at system and subsystem level. Due to the scarcity of data from the offshore wind industry, the analysis is complemented with the extensive experience from onshore structures. Trends based on the deployment parameters for the influence of design characteristics and environmental conditions on the onshore wind turbines' reliability and availability are first investigated. The estimation of the operational availability for a set of offshore wind farm scenarios allowed a comparison with the recently published performance statistics and the discussion of the integrity of the data available to date. The failure statistics of the systems deployed offshore are then discussed and compared to the onshore ones, with regard to their normalised results. The availability calculations supported the hypothesis of the negative impact of the offshore environmental conditions on the reliability figures. Nonetheless, similarities in the reliability figures of the blade adjustment system and the maintainability of the power generation and the control systems are outlined. Finally, to improve the performance prediction of future offshore projects, recommendations on the effort worth putting into research and data collection are provided

    Feasibility of machine learning algorithms for classifying damaged offshore jacket structures using SCADA data

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    The best practise for structural damage detection currently relies on the installation of structural health monitoring systems for the collection of dedicated high frequency measurements. Switching to the employment of the wind turbine's SCADA (Supervisory Control and Data Acquisition) signals and their commonly recorded low frequency statistics can lead to a reduction in the number of ad-hoc monitoring sensors and quantity of data required. In this paper, aero-hydro-servo-elastic simulations for a model of a turbine are used to assess its loads and any changes in the dynamics under healthy state and a damaged configuration case study. To prove the feasibility of the damage detection through low-resolution data, the statistics of the typically recorded signals from the SCADA and the structural monitoring systems are fed into a database for training and testing of classification algorithms. The ability of the machine learning models to generalise the classification for both stochasticity and uncertainties in the environmental conditions are tested. Decision tree-based classifiers showed the capability to capture the damage for the majority of the operating conditions considered. Though the setup of the traditional SCADA sensors had to be supplemented with an additional structural health monitoring sensor, the detection of the damage has been shown feasible by referring to low-frequency statistics only

    A Business Process Reengineering of the Surgical Path through Lean Technique: The Real Case Study of a Midsize Italian Hospital

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    This period of pandemic has had important consequences on the flow and the entire organization of any hospital. In particular, the number of accesses to the emergency room has increased, with the consequent urgent need to reorgani ze it quickly. The model proposed in this paper allows to respond to these needs by freeing not only shifts of nursing staff but also surgical staff. This workforce can then be relocated in the emergency room or of the intensive care unit who are in fact at the forefront of emergency management. The aim of this study conducted by the authors is to analyze, inside the context of a midsize Italian hospital, the actual organization model, and then to approach it by Business Process Reengineering (BPR) methodology with the goal to propose a KPI management system that evaluates the efficiency of the whole surgical path. The second objective of the study is to verify if the Operating Rooms (ORs) are properly sized to cover the surgical workload or if it would be necessary to build new ORs (answer to this question is the project mandate by Surgical Wards Chiefs). The last objective is to implement a flexible to cope with emergency situations such as a pandemic. The main result is the approximate maintenance of surgical annual activity (8169 vs 7889). The fewer resources required can be reallocated to deal with emergencies such as the current COVID-19 pandemic. In fact, the surgical shifts decreased during the test case from 464 versus 365 (-15,32%). The rooms’ utilization coefficient rose from 41% to over 52%, whereas the surgeons’ utilization coefficient rose to 61% (with values over 68% for parallel shifts). The results achieved demonstrate that improving efficiency of surgical processes is feasible and a systematic approach allows to respond to new global health challenges

    Progress on the development of a holistic coupled model of dynamics for offshore wind farms : phase II - study on a data-driven based reduced-order model for a single wind turbine

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    At present, over 1500 offshore wind turbines (OWTs) are operating in the UK with a capacity of 5.4GW. Until now, the research has mainly focused on how to minimise the CAPEX, but Operation and Maintenance (O&M) can represent up to 39% of the lifetime costs of an offshore wind farm, mainly due to the assets’ high cost and the harsh environment in which they operate. Focusing on O&M, the HOME Offshore research project (www.homeoffshore.org) aims to derive an advanced interpretation of the fault mechanisms through holistic multiphysics modelling of the wind farm. With the present work, an advanced model of dynamics for a single wind turbine is developed, able to identify the couplings between aero-hydro-servo-elastic (AHSE) dynamics and drive train dynamics. The wind turbine mechanical components, modelled using an AHSE dynamic model, are coupled with a detailed representation of a variable-speed direct-drive 5MW permanent magnet synchronous generator (PMSG) and its fully rated voltage source converters (VSCs). Using the developed model for the wind turbine, several case studies are carried out for above and below rated operating conditions. Firstly, the response time histories of wind turbine degrees of freedom (DOFs) are modelled using a full-order coupled analysis. Subsequently, regression analysis is applied in order to correlate DOFs and generated rotor torque (target degree of freedom for the failure mode in analysis), quantifying the level of inherent coupling effects. Finally, the reduced-order multiphysics models for a single offshore wind turbine are derived based on the strength of the correlation coefficients. The accuracy of the proposed reduced-order models is discussed, comparing it against the full-order coupled model in terms of statistical data and spectrum. In terms of statistical results, all the reducedorder models have a good agreement with the full-order results. In terms of spectrum, all the reduced-order models have a good agreement with the full-order results if the frequencies of interest are below 0.75Hz
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