Offshore Component Condition Monitoring via Machine Learning Enabled Smart Sensors and Low-bandwidth Data Transmission

通过支持机器学习的智能传感器和低带宽数据传输进行海上组件状态监测

• 批准号: 96936
• 金额: $ 18.32万
• 依托单位: FLICQ UK LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=96936
• 关键词: Offshore; Component; Condition; Monitoring; via

Operations and Maintenance in Offshore Wind is based currently on old models generated in the maritime sector, where routine, scheduled maintenance is carried out periodically. Industry insight has indicated a strong desire to transition to a predictive maintenance framework, based on accurate condition monitoring of individual components within an offshore wind asset. A key reason for this need is the prohibitive cost that offshore maintenance commands. Currently 20-30% of the overall expenditure for an offshore wind farm is based on Operations and Maintenance, and as the number of offshore wind farms increases and becomes even more remote, the resources (human expertise, CTV's and SOV's) will become more limited, and costs could therefore increase further.The current state of the art in the field offer sensors for monitoring aspects of mechanical systems such as vibration and acoustic emissions. A key challenge in this area is the communication required for transfer of data back to the control centre. FLICQ have developed Smart Sensor technology based on Industrial Internet of Things technology that not only collects data from the assets, but can also process and analyse sensor data to extract the meaningful information and minimise the amount of data required to be transferred back onshore to the cloud or to legacy systems. To carry out this data analysis, the FLICQ technology uses machine learning algorithms to pre-process this data for transmission. By accurately modelling how asset condition is changing, predictive models can be used to plan the best intervention time based on risk, anticipated downtime, and projected revenue loss.This project aims to demonstrate the potential of FLICQ smart systems to support the development of predictive maintenance strategies in the offshore wind sector through a practical application and product development at Levenmouth offshore demonstration turbine, owned by ORE Catapult. While FLICQ sensor technologies have been employed in a range of other sectors, the technology is yet to be implemented in the challenging offshore wind environment. The innovative technology proposed by FLICQ uses intuitive machine learning algorithms where the data is collected. This means that the data connection required for transmission to onshore control centres is low-bandwidth and low data usage. This allows the sensor to remain low-powered and have a long life in position on any component. The success FLICQ have experienced in other markets represents an opportunity to disrupt maintenance strategies in offshore wind using disruptive, Smart Sensors, powered by enabling technology.

海上风电的运营和维护目前基于海事部门产生的旧模式，定期进行例行的定期维护。行业洞察表明，基于对海上风电资产中单个组件的准确状态监测，人们强烈希望过渡到预测性维护框架。这种需求的一个关键原因是海上维护成本过高。目前，海上风电场总支出的20-30%用于运营和维护，随着海上风电场数量的增加和越来越偏远，资源（人力专业知识、CTV和SOV）将变得越来越有限，因此成本可能会进一步增加。目前，该领域的最新技术提供了用于监测机械系统（如振动和声发射）方面的传感器。这方面的一个关键挑战是将数据传输回控制中心所需的通信。FLICQ开发了基于工业物联网技术的智能传感器技术，该技术不仅可以从资产中收集数据，还可以处理和分析传感器数据，以提取有意义的信息，并最大限度地减少传输回陆上云或遗留系统所需的数据量。为了进行数据分析，FLICQ技术使用机器学习算法对数据进行预处理以进行传输。通过准确建模资产状况的变化，预测模型可以根据风险、预期停机时间和预计收入损失来规划最佳干预时间。该项目旨在通过ORE Catapult旗下Levenmouth海上示范涡轮机的实际应用和产品开发，展示FLICQ智能系统的潜力，以支持海上风电领域预测性维护策略的发展。虽然FLICQ传感器技术已经应用于其他领域，但该技术尚未在具有挑战性的海上风力环境中实施。FLICQ提出的创新技术使用直观的机器学习算法来收集数据。这意味着传输到陆上控制中心所需的数据连接是低带宽和低数据使用率的。这使得传感器保持低功耗，并具有长寿命的位置上的任何组件。FLICQ在其他市场取得的成功，代表着利用颠覆性智能传感器颠覆海上风电维护策略的机会。