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EET Fuels has deployed a centralized digital condition-monitoring program at its 10-million-ton-per-year refinery near Manchester, supported remotely by engineers in India. Developed over the past three and a half years, the initiative combines wireless sensors, online vibration monitoring, production data, and digital performance tools to identify equipment issues early. According to the company, the program helped improve refinery uptime from below 90 percent to more than 95 percent.
At the Manufacturing Innovation Conclave during ARC Advisory Group’s 24th India Forum in Bengaluru, held July 9–10, 2026, Kande Ramesh, Senior Vice President of the Global Capability Center at EET Fuels, described how the refinery built the remote monitoring model and applied it to critical rotating equipment.
Ramesh’s presentation, “Remote Machinery Surveillance using Wireless Sensors in a Global Refinery,” can be viewed on YouTube or here:
Building a Remote Operations Support Model
Drawing on its experience supporting global refining operations from India, EET Fuels established a Global Capability Center in Mumbai to provide engineering, monitoring, and analytical support to the UK facility.
Rather than pursuing a broad technology overhaul, the company initially focused on rotating-equipment reliability. Turbines, compressors, pumps, and motors have a significant influence on refinery uptime, and failures can result in production losses, extended outages, and costly recovery efforts.
The strategy focused on identifying and addressing emerging equipment issues before failure, an especially important goal in the UK, where repairs can take significantly longer because of resource and vendor constraints.
Creating a Centralized Monitoring Framework
The refinery implemented a centralized digital condition-monitoring architecture that combines multiple sources of operational and equipment data. The system integrates wireless sensors, online vibration monitoring systems, production information platforms, and digital performance-monitoring tools.
Data collected at the refinery is transmitted to specialists in Mumbai, where engineers monitor equipment health and investigate emerging issues. Information is collected at approximately 10-minute intervals, with transmission and analysis completed within 10 to 15 minutes.
A notable aspect of the initiative is that the architecture was developed largely in-house. Rather than relying extensively on external contractors, the refinery built the engineering and technology capabilities needed to support long-term operations.
Prioritizing Critical Assets
The program began with a detailed criticality assessment to identify equipment that posed the highest operational risk. Initial efforts focused on 16 critical compressors, turbines, and pumps. Monitoring coverage was later expanded to around 250 critical rotating assets, including pumps, motors, blowers, and compressors.
Although the refinery operates approximately more than 2,000 rotating assets, the team found that a much smaller group of equipment accounts for the majority of reliability risks. Concentrating resources on these assets provided a more practical and cost-effective path to performance improvement.
The monitoring framework combines vibration and temperature data with process information, allowing engineers to perform integrated diagnostics rather than evaluating machinery health in isolation. This approach helps identify whether abnormal behavior originates from mechanical issues or changing operating conditions, such as pressure and flow variations.
Preventing Failures Before They Occur
The refinery shared several examples in which early detection helped avoid potentially significant equipment failures.
In one case, engineers identified abnormal conditions affecting critical turbomachinery and detected electrostatic discharge-related issues that could have damaged bearings. The team intervened before major failures occurred.
Another case involved a critical refinery compressor, where wireless sensor data revealed developing bearing problems. Operators were able to switch equipment and address the issue before it escalated into an outage.
The refinery also highlighted a crude feed pump application in which abnormal vibration levels were traced not to equipment damage, but to changing process conditions. By adjusting pressure and flow parameters, operators restored the equipment to suitable operating conditions and avoided a potential breakdown.
According to the presentation, the refinery previously experienced four failures involving crude pumps within a three-month period. Since implementing the monitoring strategy, the site has operated for more than three years without another similar failure.
Gradual Progress Toward AI-Driven Diagnostics
While AI was a major theme of the conference, Ramesh emphasized that the refinery is still developing its AI capabilities. AI-assisted diagnostic models are being piloted, but the program remains at a relatively early stage.
The refinery estimates that approximately 20–25 percent of its diagnostic workflow currently incorporates AI-based capabilities, with a longer-term goal of reaching about 80 percent.
The team has also developed agents using Microsoft Copilot technologies to generate alerts and provide recommended actions. However, subject matter experts continue to test, review and validate those recommendations before implementation.
Alongside these efforts, the refinery has built performance-monitoring models that track parameters such as compressor efficiency, operating points, and equipment health. While Ramesh compared some capabilities to elements of a digital twin, he noted that additional development is required before the system can be described as a full digital twin implementation.
EET Fuels combines remote engineering expertise, wireless sensors, and equipment data to monitor critical rotating assets at its UK refinery
Reliability as a Strategic Imperative
The refinery reported measurable improvements from the initiative, including reduced unplanned downtime, fewer recurring equipment issues, and an increase in mean time between failures from 45 months to 53 months.
For operators of aging industrial facilities, the project provides an example of how centralized expertise, condition monitoring, Industrial IoT sensors, data analytics, and emerging AI tools can support operational resilience. By focusing on critical assets and integrating equipment and process data into a unified monitoring strategy, EET Fuels has linked digital transformation efforts to improved reliability, higher uptime, and stronger operational performance.