Digital Technologies Revolutionising Oil Condition Monitoring

Whilst it may not sound particularly exciting, oil condition monitoring is an essential process for any business that operates or manufactures rotating equipment such as turbines in power generating plants, gearboxes in wind turbines, and engines on shipping vessels. As a practice, it involves analysis of the physical and chemical properties of the lubricating oil used in rotating equipment to determine equipment health and identify potential problems. Just like many other processes, advances in connectivity (IoT), data acquisition and analytics have allowed oil condition monitoring to become more efficient and cost-effective. In this post, Rotimi Alabi, RAB-Microfluidics founder and CEO, discusses three digital technologies revolutionising oil condition monitoring.

Internet of Things (IoT) Technology enabling real-time data gathering

A term that is beginning to become more commonplace within oil condition monitoring is real-time data analysis. What this means is that technology now allows for data to be delivered immediately after sampling. Previously, lubricating oil samples needed to be transported offsite for laboratory analysis before the results are generated to establish equipment condition. Now technology companies like RAB-Microfluidics have developed technologies that automate the lubricating oil analysis process to generate health condition data in real-time. This removes the need to send lubricating oil samples offsite and allows for quicker reaction to contamination, degradation, wear, and incipient faults. What makes real-time data gathering possible is the evolution of IoT tech. More companies are adopting IoT tech into their equipment and services. IoT tech allows companies to collect real-time data and analyse equipment condition more effectively. RAB Microfluidics Oleum Oracle device is one such device which utilises IoT sensors to provide real-time data.

IoT Technology is increasing in prominence.
Use of Artificial Intelligence (AI) and Machine Learning (ML) to enable Condition-based Maintenance (CbM) and Predictive Maintenance (PdM)

When discussing oil condition monitoring, the terms Condition-based Maintenance (CbM) and Predictive Maintenance (PdM) will inevitably appear. It is the use of data analysis to maintain equipment in real-time based on its condition (CbM) or predict when maintenance should be performed on equipment (PdM). Equipment failures and maintenance can incur huge costs for companies. CbM and PdM can therefore prevent these risks and become even more powerful by utilising AI and ML to detect issues early on and avoid catastrophes.

The implementation of AI and ML protocols on equipment condition data generated by IoT tech can prove transformational. Indeed, their benefits cannot be understated. AL & ML provide algorithms that can analyse real-time data and identify potential patterns or anomalies that could otherwise prove catastrophic for equipment. They can be used to automate the analytics process which saves time, further cut down costs and minimise the risk of human error. One example of this is hydraulic systems in offshore drilling platforms, which use AI and ML to monitor oil. This tech thereby analyses data in real-time to detect and predict when equipment could break down.

Artificial Intelligence and Machine Learning provide enormous benefits.
Cloud-based services that allow remote and real-time access

Cloud-based services and solutions are becoming increasingly ubiquitous. The opportunity to store and access data remotely is simply too advantageous to ignore. Moreover, they provide wireless integration, which makes it significantly easier to integrate with existing systems. Our Oleum Oracle device contains a cloud analytics platform where data can be accessed from remote locations. Services like this enable companies to make decisions anywhere and everywhere.

RAB-Microfluidics’ Oleum Oracle Device.
Looking forward

Whilst we cannot always accurately predict the future, oil condition monitoring will remain a key process in many industries, and it will continue to change. What we can say is that the use of IoT technology, implementation of AI and ML algorithms, real-time data analysis, and cloud-based services and solutions are enabling advances in both effectiveness and efficiency of condition-based and predictive maintenance strategies. Furthermore, there is a growing emphasis from companies to reduce their carbon footprint in operations. Thus, sustainability will become increasingly important. Regardless, companies must continue to take initiative and adopt innovative technologies to remain competitive.

Sustainability must be a key ethos for companies moving forward.

RAB-Microfluidics is pioneering the automation of chemical and physical compositional analysis of lubricating oil with its microfluidic lab-on-a-chip technology to tackle the challenges of equipment reliability and availability.

As a Research & Development company, we’re constantly pushing the boundaries of what’s possible to deliver innovative solutions that meet the needs of our clients.

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