This is the peer reviewed version of the following article: Whittle, M., Trevelyan, J., Shin, W. and Tavner, P. (2013), Improving wind turbine drivetrain bearing reliability through pre-misalignment. Wind Energy, 17 (8): 12171230 which has been published in nal form at http://dx.doi.org/10.1002/we.1629. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.Additional information:
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AbstractImproving the reliability of wind turbines (WT) is an essential component in the bid to minimise the cost of energy, especially for offshore wind due to the difficulties associated with access for maintenance. Numerous studies have shown that WT gearbox and generator failure rates are unacceptably high, particularly given the long downtime incurred per failure. There is evidence that bearing failures of the gearbox high speed stage (HSS) and generator account for a significant proportion of these failures. However, the root causes of these failure data are not known and there is, therefore, a need for fundamental computational studies to support the valuable 'top down' reliability analyses. In this paper a real (proprietary) 2 MW geared WT was modelled in order to compute the gearbox-generator misalignment and predict the impact of this misalignment upon the gearbox HSS and generator bearings. At rated torque misalignment between the gearbox and generator of 8500 µm was seen. For the 2 MW WT analysed the computational data show that the L 10 fatigue lives of the gearbox HSS bearings were not significantly affected by this misalignment but that the L 10 fatigue lives of the generator bearings, particularly the drive-end bearing, could be significantly reduced. It is proposed to apply a nominal offset to the generator in order to reduce the misalignment under operation thereby reducing the loading on the gearbox HSS and generator bearings. The value of performing integrated systems analyses has been demonstrated and a robust methodology has been outlined.
In submarine warfare systems, passive SONAR is commonly used to detect enemy targets while concealing one’s own submarine. The bearing information of a target obtained from passive SONAR can be accumulated over time and visually represented as a two-dimensional image known as a BTR image. Accurate measurement of bearing–time information is crucial in obtaining precise information on enemy targets. However, due to various underwater environmental noises, signal reception rates are low, which makes it challenging to detect the directional angle of enemy targets from noisy BTR images. In this paper, we propose a deep-learning-based segmentation network for BTR images to improve the accuracy of enemy detection in underwater environments. Specifically, we utilized the spatial convolutional layer to effectively extract target objects. Additionally, we propose novel loss functions for network training to resolve a strong class imbalance problem observed in BTR images. In addition, due to the difficulty of obtaining actual target bearing data as military information, we created a synthesized BTR dataset that simulates various underwater scenarios. We conducted comprehensive experiments and related discussions using our synthesized BTR dataset, which demonstrate that the proposed network provides superior target segmentation performance compared to state-of-the-art methods.
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