Developing Microfluidic Lab-on-a-chip Technology Demonstrator for Onsite Lubricating Oil Analysis

开发用于现场润滑油分析的微流控芯片实验室技术演示器

• 批准号: 133263
• 金额: $ 12.2万
• 依托单位: RAB MICROFLUIDICS RESEARCH AND DEVELOPMENT COMPANY LIMITED
• 依托单位国家: 英国
• 项目类别: Feasibility Studies
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=133263
• 关键词: Developing; Microfluidic; Lab; chip; Technology

The automation of industrial practices to enable greater productivity on production floors is driving the need to replace conventional processes. One of such processes is the use of conventional laboratories to determine the rate of wear and degradation of lubricated production floor machinery. The inefficiency of this process results in reactive maintenance strategies where machinery is maintained only after it has broken-down thus reducing machine availability and productivity. Another is carrying out maintenance when there is no need for this as is the case with preventative maintenance strategies, making maintenance of machinery unnecessarily expensive. Diagnosing early, potential failure of heavy machinery is critical to operations across many industries. For this reason, industrial businesses in 2016 spent £2.01bn on state-of-the-art Oil Condition Monitoring (OCM) techniques. These techniques however, are inefficient, expensive and environmentally unfriendly, for example, costing additional £2.1bn in breakdowns, repairs and downtime losses. 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, drive trains 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 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 businesses in the manufacturing space 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 many business in the manufacturing space, using the data we generate intelligently and innovatively to forward model machinery 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 productivity on production floors. We extend our capabilities to other industries such as transportation, power generation, maritime etc. helping to transition businesses in these industries to predictive maintenance strategies.

工业实践的自动化使生产车间的生产率更高，这推动了对传统工艺的替代需求。其中一个过程是使用传统的实验室来确定润滑的生产车间机械的磨损和降解速率。这一过程的低效率导致反应性维护策略，其中机械仅在其发生故障后进行维护，从而降低了机器的可用性和生产率。另一个是在不需要的时候进行维护，就像预防性维护策略一样，这使得机器的维护不必要地昂贵。早期诊断重型机械的潜在故障对许多行业的运营至关重要。出于这个原因，工业企业在2016年花费了20.1亿英镑用于最先进的油况监测（OCM）技术。然而，这些技术效率低下，昂贵且不环保，例如，在故障，维修和停机损失方面额外花费21亿英镑。RAB-Microfluidics开发了尖端的微流体芯片实验室技术，可对润滑油进行实时连续测试和分析。我们的“芯片实验室”技术提供的油分析速度比当前的“将样品送到实验室”方法快1000倍，成本低10倍。分析发动机油、传动系统等中的污染物是一种行之有效的检测问题的方法。此程序称为油液状态监测。我们在现场提供真实的时间，节省成本并提高设备可靠性。我们将联合收割机的硬件技术与数据计算相结合，通过开发机器学习功能来利用硬件产生的数据。这为客户提供了实时连续监控、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程度的资金，以展示基于实验室的原型技术。尽管如此，该项目旨在以此为基础，开发一个现场演示器，让企业在现场试验中参与制造领域，并实现我们的技术可以提供的价值。这项技术将使我们能够解决制造领域许多企业难以触及和难以感知的挑战，使用我们智能和创新地生成的数据来预测机器行为，并使企业沉浸在工业4.0中。我们推进维护策略的发展，以确保设备的可靠性，提高整体设备效率（OEE），并通过生产车间的生产力。我们将我们的能力扩展到其他行业，如运输，发电，海事等，帮助这些行业的企业过渡到预测性维护策略。