Tidal Steam Turbine blade fault diagnosis using time-frequency analyses

Tidal Stream Turbines are developing renewable energy devices, for which proof of concept commercial devices are been deployed. The optimisation of such devices is supported by research activities. Operation within selected marine environments will lead to extreme dynamic loading and other problems. Further, such environments emphasise the need for condition monitoring and prognostics to support difficult maintenance activities. This paper considers flow and structural simulation research and condition monitoring evaluations. In particular, reduced turbine blade functionality will result in reduced energy production, long down times and potential damage to other critical turbine sub-assemblies. Local sea conditions and cyclic tidal variations along with shorter timescale dynamic fluctuations lead to the consideration of time-frequency methods. This paper initially reports on simulation and scale-model experimental testing of blade-structure interactions observed in the total axial thrust signal. The assessment is then extended to monitoring turbine blade and rotor condition, via drive shaft torque measurements. Parametric models are utilised and reported and a motor-drive train-generator test rig is described. The parametric models allow the generation of realistic time series used to drive this test rig and hence to evaluate the applicability of various time-frequency algorithms to the diagnosis of blade faults

Performance and condition monitoring of tidal stream turbines

Research within the Cardiff Marine Energy Research Group (CMERG) has considered the integrated mathematical modelling of Tidal Stream Turbines (TST). The modelling studies are briefly reviewed. This paper concentrates on the experimental validation testing of small TST models in a water flume facility. The dataset of results, and in particular the measured axial thrust signals are analysed via timefrequency methods. For the 0.5 m diameter TST the recorded angular velocity typically varies by ± 2.5% during the 90 second test durations. Modelling results confirm the expectations for the thrust signal spectrums, for both optimum and deliberately offset blade results. A discussion of the need to consider operating conditions, condition monitoring sub-system refinements and the direction of prognostic methods development, is provided

Performance and condition monitoring of tidal stream turbines

Research within the Cardiff Marine Energy Research Group (CMERG) has considered the integrated mathematical modelling of Tidal Stream Turbines (TST). The modelling studies are briefly reviewed. This paper concentrates on the experimental validation testing of small TST models in a water flume facility. The dataset of results, and in particular the measured axial thrust signals are analysed via timefrequency methods. For the 0.5 m diameter TST the recorded angular velocity typically varies by ± 2.5% during the 90 second test durations. Modelling results confirm the expectations for the thrust signal spectrums, for both optimum and deliberately offset blade results. A discussion of the need to consider operating conditions, condition monitoring sub-system refinements and the direction of prognostic methods development, is provided

Effects of wave-current interactions on the performance of tidal stream turbines

The main objective of this paper is to analyse extreme cases of wave-current interactions on tidal stream energy converters. Experiments were undertaken in the INSEAN tow tank facility where flow velocities of 0.5 and 1m/s were used with and without waves. The wave variations studied in this testing campaign were between wave heights of 0.2 to 0.4m with a 2s wave period. These wave conditions were considered extreme cases considering the use of a turbine with a rotor diameter of 0.5m. The turbine was equipped with a torque transducer, an encoder and a strain gauge to measure power coefficients and forces on a single blade root. Therefore, the results of this experiment are used to improve the understanding of wave effects on tidal stream rotors by analysing not only the temporal variations of power and blade loading but also the peak variations of them

Robotics Platforms Incorporating Manipulators Having Common Joint Designs

Manipulators in accordance with various embodiments of the invention can be utilized to implement statically stable robots capable of both dexterous manipulation and versatile mobility. Manipulators in accordance with one embodiment of the invention include: an azimuth actuator; three elbow joints that each include two actuators that are offset to allow greater than 360 degree rotation of each joint; a first connecting structure that connects the azimuth actuator and a first of the three elbow joints; a second connecting structure that connects the first elbow joint and a second of the three elbow joints; a third connecting structure that connects the second elbow joint to a third of the three elbow joints; and an end-effector interface connected to the third of the three elbow joints

Callipeltosides A, B and C: Total Syntheses and Structural Confirmation.

Since their isolation almost 20 years ago, the callipeltosides have been of long standing interest to the synthetic community owing to their unique structural features and inherent biological activity. Herein we present our full research effort that has led to the synthesis of these molecules. Key aspects of our final strategy include 1) synthesis of the C1-C9 pyran core (5) using an AuCl3 -catalysed cyclisation; 2) formation of C10-C22 vinyl iodide (55) by sequential bidirectional Stille reactions and 3) diastereoselective union of these advanced fragments by means of an alkenylzinc addition (d.r.=91:9 at C9). The common callipeltoside aglycon (4) was completed in a further five steps. Following this, all three sugar fragments were appended to provide the entire callipeltoside family. In addition to this, D-configured callipeltose B was synthesised and appended to the callipeltoside aglycon. The (1) H NMR spectrum of this molecule was found to be significantly different to the natural isolate, further supporting our assignment of callipeltoside B (2).We thank Novartis for a research studentship (J.R.F) and also gratefully acknowledge the EPSRC (Award numbers: EP/F06985/1; EP/K009494/1; EP/K039520/1) for financial support (C.M.P., T.N.S., R.A.B., J.G. and D.M.S).This is the final version. It first appeared at http://dx.doi.org/10.1002/chem.20150187

Automated interpretation of systolic and diastolic function on the echocardiogram:a multicohort study

Background: Echocardiography is the diagnostic modality for assessing cardiac systolic and diastolic function to diagnose and manage heart failure. However, manual interpretation of echocardiograms can be time consuming and subject to human error. Therefore, we developed a fully automated deep learning workflow to classify, segment, and annotate two-dimensional (2D) videos and Doppler modalities in echocardiograms. Methods: We developed the workflow using a training dataset of 1145 echocardiograms and an internal test set of 406 echocardiograms from the prospective heart failure research platform (Asian Network for Translational Research and Cardiovascular Trials; ATTRaCT) in Asia, with previous manual tracings by expert sonographers. We validated the workflow against manual measurements in a curated dataset from Canada (Alberta Heart Failure Etiology and Analysis Research Team; HEART; n=1029 echocardiograms), a real-world dataset from Taiwan (n=31 241), the US-based EchoNet-Dynamic dataset (n=10 030), and in an independent prospective assessment of the Asian (ATTRaCT) and Canadian (Alberta HEART) datasets (n=142) with repeated independent measurements by two expert sonographers. Findings: In the ATTRaCT test set, the automated workflow classified 2D videos and Doppler modalities with accuracies (number of correct predictions divided by the total number of predictions) ranging from 0·91 to 0·99. Segmentations of the left ventricle and left atrium were accurate, with a mean Dice similarity coefficient greater than 93% for all. In the external datasets (n=1029 to 10 030 echocardiograms used as input), automated measurements showed good agreement with locally measured values, with a mean absolute error range of 9–25 mL for left ventricular volumes, 6–10% for left ventricular ejection fraction (LVEF), and 1·8–2·2 for the ratio of the mitral inflow E wave to the tissue Doppler e' wave (E/e' ratio); and reliably classified systolic dysfunction (LVEF <40%, area under the receiver operating characteristic curve [AUC] range 0·90–0·92) and diastolic dysfunction (E/e' ratio ≥13, AUC range 0·91–0·91), with narrow 95% CIs for AUC values. Independent prospective evaluation confirmed less variance of automated compared with human expert measurements, with all individual equivalence coefficients being less than 0 for all measurements. Interpretation: Deep learning algorithms can automatically annotate 2D videos and Doppler modalities with similar accuracy to manual measurements by expert sonographers. Use of an automated workflow might accelerate access, improve quality, and reduce costs in diagnosing and managing heart failure globally. Funding: A*STAR Biomedical Research Council and A*STAR Exploit Technologies