Productizing Microfluidic Technology for Real-time Lubricating Oil Analysis on Wind Turbines

海上风电行业运营和维护(O&M)实践的自动化是降低成本的核心。海上风电场的偏远位置意味着任何人为干预的要求都会推高运营和维护成本。这使得将海上风能送到我们家的成本在英国排名第二。到目前为止，向风电场项目所有者和原始设备制造商(OEM)提供有关变速箱和传动系统等关键涡轮机部件的关键机器健康信息的润滑油分析过程一直很难自动化。这导致仅去年一年全球就有约3.2万个变速箱发生故障。这样的故障本可以通过正确的技术及早发现。及早诊断风力涡轮机部件的潜在故障对涡轮机的运行至关重要。微流控开发了尖端的微流控芯片实验室技术，为润滑油提供实时、连续的测试和分析。我们的“芯片实验室”技术提供的油液分析速度比目前的“将样品送到实验室”方法快1000倍，成本低10倍。分析发动机油、变速箱、传动系等中的污染物是一种成熟的检测问题的方法。这一过程称为油品状态监测。我们在现场实时交付，节省了成本并提高了设备可靠性。我们通过开发机器学习能力来利用硬件产生的大数据，将我们的硬件技术与数据计算结合起来。这为客户提供了实时连续的监测、早期问题诊断、快速决策、提高效率和节省成本。到目前为止，我们已经收到了不同水平的资金，用于用基于实验室的原型来演示这项技术。尽管如此，该项目寻求在此基础上开发一个现场演示程序，以吸引项目所有者和原始设备制造商进行现场试验，并了解我们的技术可以提供的价值。这项技术将使我们能够解决风电行业难以触及和难以感知的挑战，使用我们以智能和创新的方式生成的数据来预测模型涡轮机的行为，并使企业沉浸在行业4.0中。我们推进了维护策略的发展，以确保设备可靠性，提高整体设备效率(OEE)，并通过扩展涡轮机的可靠性。这可以减少对涡轮机进行物理干预的需要，并有效地降低运行和维护成本。这将潜在地降低海上风能的电力成本，使海上风能相对于其他电力来源更具竞争力，并对我们的电费产生涟漪效应。“

"The automation of Operation and Maintenance (O&M) practices in offshore wind sector is central to driving lower costs. The remote location of offshore wind farms means any requirement for physical human intervention pushes O&M costs upwards. This contributes to making the cost of getting offshore wind energy to our homes the second highest in the UK. Until now, it has been difficult to automate lubricating oil analysis processes that provide wind farm project owners and Original Equipment Manufacturer's (OEMs) crucial machine health information on key turbine components such as the gearbox and drivetrain. This resulted in breakdown of about 32,000 gearboxes globally last year alone. Such breakdowns could have been detected early by the right technology.Diagnosing early, potential failure of component parts in a wind turbine is critical to turbine operations. RAB-Microfluidics has developed cutting edge microfluidic lab-on-a-chip technology to deliver real-time continuous testing and analysis of lubricating oil. Our ""Lab-on-a-Chip"" technology delivers oil analysis 1000x faster and 10x cheaper than the current ""send the sample to the Laboratory"" approach. Analysis of contaminants in engine oil, gearboxes, drivetrains etc. is a well-established method of detecting problems. This procedure is called Oil Condition Monitoring. We deliver this onsite, in real time, saving cost and improving equipment reliability. We combine our hardware technology with data computing by developing machine learning capabilities to utilise the big data generated from our hardware. This offers customers real-time continuous monitoring, early problem diagnosis, rapid decision making, enhanced efficiency and cost savings.To date we have received various levels of funding to demonstrate the technology with laboratory based prototypes. Nonetheless, this project seeks to build on this and develop a field demonstrator to engage project owners and OEMs in field trials and in the reality of the value our technology can provide. This technology will enable us to solve the hard-to-reach and hard-to-sense challenges of the wind sector, using the data we generate intelligently and innovatively to forward model turbine behaviour and immerse businesses in industry 4.0\. We advance evolution of maintenance strategies to secure equipment reliability, increase Overall Equipment Effectiveness (OEE) and by extension reliability of turbines. This can reduce the need for physical intervention on turbines and effectively lower O&M costs. This will potentially reduce electricity costs from offshore wind, making offshore wind more competitive with other sources of electricity and ripple in effect to our electricity bills."