Reliability and availability of shipping vessels is critical to quality of life and impacts significantly on cost of goods consumed globally. This is because the maritime industry is responsible for 90% of global trade, hence disruptions in maritime service impacts the way we live and how much we pay for our goods. Until now, it has been difficult to predict with any certainty machinery failure in shipping vessels. This has resulted in high rate of failure in rotating lubricated machinery such as engines. The high rate of marine engine failure is the leading cause of service disruptions in the maritime industry. However, such disruptions could be avoided with early detection of engine faults ensuring that engine failure is prevented. Diagnosing early, potential failure of marine lubricated equipment such as engines is critical to operation of a shipping vessel. 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, etc. is a well-established method of detecting problems. This procedure is called Oil Condition Monitoring. We deliver this onsite, in real time and this is a significant improvement on the current practice of sending the sample to onshore laboratories for analysis thus saving cost and improving machinery reliability and vessel availability. We combine our hardware technology with data computing by developing machine learning capabilities to utilise the big data generated from our hardware. This offers real-time continuous monitoring, early problem diagnosis, rapid decision making, enhanced efficiency and cost savings. This project seeks to develop this core technology further and build a field prototype demonstrator that integrates with a live operational marine engine. This will ensure we fully demonstrate the automation of our novel Oil Condition Monitoring process. This will be a first-of-its-kind development with potential to dramatically improve shipping vessel reliability and availability by ensuring developing faults of key equipment are identified early. If successful, our technology will herald a game changer for the maritime sector and would invariably have a ripple effect on our quality of life by ensuring reduced marine service disruptions. There is also the added possibility of such success being reflected on the cost of good we consumed due to lower marine transportation costs.

Greenhouse gas (GHG) emissions from maritime transport is estimated at around 1 Bn ton of carbon dioxide equivalents (CO2 eq.) accounting for 3% of global anthropogenic emissions (IMO 2020). Emissions need to be cut by 50% up to 2050 to meet the Paris agreements (IMO 2020). Automation and digitisation have gained significant traction as enablers of greater efficiency in the maritime industry and can contribute meaningfully to reducing GHG emissions. RAB-Microfluidics have developed a microfluidic lab-on-a-chip technology that automates and digitises a key aspect of a shipping vessel operation -- Oil Condition Monitoring (OCM) which has the potential to enable marine engine efficiency, thereby reducing GHG emissions. OCM has remained relatively unchanged for over a hundred years with conventional wet chemistry techniques being the gold standard for industrial testing and analysis for engine condition monitoring. While these techniques deliver, robust compositional information, they are unable to provide to the desired frequency (because it is a manual process) data that can enable key insights to aid efficiencies and energy savings that lead to reduction in GHG emissions. RAB-Microfluidics lab-on-a-chip technology completely changes this as our technology automates the OCM process. Consequently, there is now the opportunity to plug that existing knowledge gap. In this project, we will integrate this technology with a live marine engine to continuously monitor the condition of engine condition during operation in real-time. Engine condition data will then be correlated with engine operation information (e.g., engine load, exhaust temperatures, shaft power/torque, engine speed, fuel pressure, etc.), performance data (e.g., energy efficiency plan, lube-oil consumption, fuel consumption etc.) and GHG emissions data to establish a cause-and-effect relationship. Such automation will allow generation of continuous streams of data on engine condition to provide insights that ensure the optimisation of marine engine operation/performance, permitting energy savings which will lead to reduction of GHG emissions. This potentially creates an offering that does not currently exist. This automation of the OCM process permits a potentially disruptive offering that transitions businesses from reactive to predictive operational strategies with the added potential to open up a £400Mn OCM automation market. This will be a first-of-its-kind development with potential to impact the reduction of GHG emissions. If successful, our technology will herald a stepwise change for the maritime sector helping maritime companies transition to next zero emission.

"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."

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.