Is laboratory testing of SCC susceptibility fit for purpose?

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Applications of machine learning in diagnostics and prognostics of wind turbine high speed generator failure

The cost of wind energy has decreased over the last decade as technology has matured and the industry has benefited greatly from economies of scale. That being said, operations and maintenance still make up a significant proportion of the overall costs and needs to be reduced over the coming years as sites, particularly offshore, get larger and more remote. One of the key tools to achieve this is through enhancements of both SCADA and condition monitoring system analytics, leading to more informed and optimised operational decisions. Specifically examining the wind turbine generator and highspeed assembly, this thesis aims to showcase how machine learning techniques can be utilised to enhance vibration spectral analysis and SCADA analysis for early and more automated fault detection. First this will be performed separately based on features extracted from the vibration spectra and performance data in isolation before a framework will be presented to combine data sources to create a single anomaly detection model for early fault diagnosis. Additionally by further utilising vibration based analysis, machine learning techniques and a synchronised database of failures, remaining useful life prediction will also be explored for generator bearing faults, a key component when it comes to increasing wind turbine generator reliability. It will be shown that through early diagnosis and accurate prognosis, component replacements can be planned and optimised before catastrophic failures and large downtimes occur. Moreover, results also indicate that this can have a significant impact on the costs of operation and maintenance over the lifetime of an offshore development.The cost of wind energy has decreased over the last decade as technology has matured and the industry has benefited greatly from economies of scale. That being said, operations and maintenance still make up a significant proportion of the overall costs and needs to be reduced over the coming years as sites, particularly offshore, get larger and more remote. One of the key tools to achieve this is through enhancements of both SCADA and condition monitoring system analytics, leading to more informed and optimised operational decisions. Specifically examining the wind turbine generator and highspeed assembly, this thesis aims to showcase how machine learning techniques can be utilised to enhance vibration spectral analysis and SCADA analysis for early and more automated fault detection. First this will be performed separately based on features extracted from the vibration spectra and performance data in isolation before a framework will be presented to combine data sources to create a single anomaly detection model for early fault diagnosis. Additionally by further utilising vibration based analysis, machine learning techniques and a synchronised database of failures, remaining useful life prediction will also be explored for generator bearing faults, a key component when it comes to increasing wind turbine generator reliability. It will be shown that through early diagnosis and accurate prognosis, component replacements can be planned and optimised before catastrophic failures and large downtimes occur. Moreover, results also indicate that this can have a significant impact on the costs of operation and maintenance over the lifetime of an offshore development

Solution conductivity dependent crack size effect in stress corrosion cracking and corrosion fatigue

The chemical crack size effect on environmentally assisted crack growth was first demonstrated experimentally by Gangloff [1] and supported on a more robust theoretical framework by Turnbull et al. [2,3]. It is probably better dubbed the electrochemical crack size effect since the potential drop in the crack was a critical factor in determining the solution chemistry and the sensitivity to crack size. In recent experimental studies [4] we have focused on the growth rate of small and long stress corrosion and corrosion fatigue cracks in 12Cr steam turbine blade steels in low conductivity water containing 35 ppm Cl- (simulating upset steam condensate chemistry). A large effect of crack size on growth rate was observed for the same mechanical driving force. However, the crack-size effect disappeared in lower conductivity solution, 300 ppb Cl- and 300 ppb SO42- (corresponding to normal steam condensate chemistry). Furthermore, corrosion fatigue long crack growth rates were the same in aerated and in deaerated solutions for the two environments but stress corrosion cracks arrested in deaerated solution. An explanation for these varied results will be presented based on the concept of the solution conductivity dependent crack size effect and its impact on potential drop and the crack-tip potential. To underpin this conceptual idea and to explore further the scale of this effect for varied crack size and solution conductivity combinations, modelling of crack electrochemistry is being undertaken and the preliminary results will be discussed. R.P. Gangloff, The criticality of crack size in aqueous corrosion fatigue, Res. Mech. Let., 1981, 1, 299-306. A. Turnbull and J.G.N. Thomas, A model of crack electrochemistry for steels in the active state based on mass transport by diffusion and ion migration , J Electrochemical Society, 129 (7), 1412-1422, 1982. A. Turnbull and D.H. Ferriss, Mathematical modelling of the electrochemistry in corrosion fatigue cracks in steel corroding in sea water . Corrosion Science, 27 (12), 1323-1350, 1987. S. Zhou, M. Lukaszewicz and A. Turnbull, Small and short crack growth and the solution-conductivity dependent electrochemical crack size effect, Corros. Sci., 97 (2015) 25-37

Do corrosion pits eliminate the benefit of shot-peening?

Shot peening is used in many industrial applications, e.g. steam turbine blades, to induce near-surface compressive residual stresses and reduce the likelihood of failure by fatigue, corrosion fatigue and stress corrosion cracking. On the whole, shot peening has proven to be very successful in increasing the life of structures and components. However, the depth of the compressive stress layer is typically only about 250 µm and this poses the question as to the retained benefit when corrosion pits develop to varying depth. In the first stage to addressing this issue we show that the fatigue limit of a 12 Cr martensitic stainless steel turbine blade material tested in air at varying pit depths, ranging from 50 µm to 320 µm, was still significantly enhanced by shot peening even for the maximum depth studied. Complementary measurement of the crack propagation rate from a corrosion pit showed that the propagation rate was retarded by the near-surface compressive stress for crack depths up to 0.9 mm, well beyond the depth of the compressive layer. Serial sectioning to identify the loci of crack initiation sites yielded the unexpected result that crack development occurred preferentially away from the pit base, especially for the smaller pit depths. Finite element analysis to predict the stress and strain around a corrosion pit and to estimate the stress intensity factor will be described as a basis for rationalising the experimental observations. Please click Additional Files below to see the full abstract

Combining SCADA and vibration data into a single anomaly detection model to predict wind turbine component failure

Reducing downtime through predictive or condition-based maintenance is a promising strategy to help reduce costs associated with wind farm operation and maintenance. To help effectively monitor wind turbine condition, operators now rely on multiply sources of data to make informed operational decisions which can minimise downtime, increasing availability and profitability of any given site. Two of such approaches are SCADA temperature and vibration monitoring, which are typically performed in isolation and compared over time for both fault diagnostics and reliability analysis. Presenting two separate case studies, this paper describes a methodology to bring multiple data sources together to diagnose faults by using a single-class support vector machine classifier to assess normal behaviour model error, with results showing that anomalies can be detected more consistently when compared to more standard approaches of analysing each data source in isolation

The Earth by TELEMAC

Water Qualit

Tropical Cyclone Modelling with TELEMAC-2D

Hydrodynamic

On the Development of Offshore Wind Turbine Technology:An Assessment of Reliability Rates and Fault Detection Methods in a Changing Market

Offshore wind turbine drive train technology is evolving as developers increase size, aim to maximise availability and adapt to changing electricity grid requirements. This work first of all explores offshore technology market trends observed in Europe, providing a comprehensive overview of installed and planned capacity, showing a clear shift from smaller high-speed geared machines to larger direct-drive machines. To examine the implications of this shift in technology on reliability, stop rates for direct-drive and gear-driven turbines are compared between 39 farms across Europe and South America. This showed several key similarities between configurations, with the electrical system contributing to largest amount of turbine downtime in either case. When considering overall downtime across all components, the direct-drive machine had the highest value, which could be mainly attributed to comparatively higher downtime associated with the electrical, generator and control systems. For this study, downtime related to the gearbox and generator of the gear-driven turbine was calculated at approximately half of that of the direct-drive generator downtime. Finally, from a perspective of both reliability and fault diagnostics at component level, it is important to understand the key similarities and differences that would allow lessons learned on older technology to be adapted and transferred to newer models. This work presents a framework for assessing diagnostic models published in the literature, more specifically whether a particular failure mode and required input data will transfer well between geared and direct-drive machines. Results from 35 models found in the literature shows that the most transferable diagnostic models relate to the hydraulic, pitch and yaw systems, while the least transferable models relate to the gearbox. Faults associated with the generator, shafts and bearings are failure mode specific, but generally require some level of modification to adapt features to available data

Effect of time history on normal behaviour modelling using SCADA data to predict wind turbine failures

Operations and Maintenance (O&M) can make up a significant proportion of lifetime costs associated with any wind farm, with up to 30% reported for some offshore developments. It is increasingly important for wind farm owners and operators to optimise their assets in order to reduce the levelised cost of energy (LCoE). Reducing downtime through condition-based maintenance is a promising strategy of realising these goals. This is made possible through increased monitoring and gathering of operational data. SCADA data are useful in terms of wind turbine condition monitoring. This paper aims to perform a comprehensive comparison between two types of normal behaviour modelling: full signal reconstruction (FSRC) and autoregressive models with exogenous inputs (ARX). At the same time, the effects of the training time period on model performance are explored by considering models trained with both 12 and 6 months of data. Finally, the effects of time resolution are analysed for each algorithm by considering models trained and tested with both 10 and 60 min averaged data. Two different cases of wind turbine faults are examined. In both cases, the NARX model trained with 12 months of 10 min average Supervisory Control And Data Acquisition (SCADA) data had the best training performance

Evaporation, seepage and water quality management in storage dams: a review of research methods

One of the most significant sources of water wastage in Australia is loss from small storage dams, either by seepage or evaporation. Over much of Australia, evaporative demand routinely exceeds precipitation. This paper outlines first, methodologies and measurement techniques to quantify the rate of evaporative loss from fresh water storages. These encompass high-accuracy water balance monitoring; determination of the validity of alternative estimation equations, in particular the FAO56 Penman- Monteith ETo methodology; and the commencement of CFD modeling to determine a 'dam factor' in relation to practical atmospheric measurement techniques. Second, because the application of chemical monolayers is the only feasible alternative to the high cost of physically covering the storages to retard evaporation, the use of cetyl alcohol-based monolayers is reviewed, and preliminary research on their degradation by photolytic action, by wind break-up and by microbial degradation reported. Similarly, preliminary research on monolayer visualisation techniques for field application is reported; and potential enhancement of monolayers by other chemicals and attendant water quality issues are considered