Powering Predictive Maintenance in the Refinery with Generative AI

Challenge

Like many processing facilities operating with aging infrastructure, the client faced a problem of excessive maintenance, performed reactively. While failures did not occur daily, they resulted in substantial expenses, and more critically, there was no reliable prediction system to identify which assets were at risk. The maintenance planning process stretched over weeks, while manual inspections consumed significant labor time without effectively reducing unplanned downtime. The company was facing internal resistance to the implementation of new technologies, mainly due to unclear ROI from previous digitalization attempts.

The operator was struggling with three main issues:

• The refinery’s reliance on manual, reactive maintenance processes that were time-consuming, resource-intensive, and prone to delays and inefficiencies. Frequent manual inspections were required for thousands of assets, significantly increasing labor costs and reducing operational uptime.
• Maintenance planning cycles averaged 80 days due to fragmented data sources, lack of real-time equipment insights, and lack of integration between sensor data, anomaly detection tools, and enterprise system and permit workflows.
• The scarcity of high-quality labeled anomaly data had been a major obstacle to the successful deployment of traditional machine learning solutions for predictive maintenance, as it led to unreliable failure prediction and undermined the effectiveness of earlier digital transformation efforts.

The challenge was to design and build a solution that would not only alert the user but also demonstrate planning time reduction and inspection costs optimization in a way that operators, planners, and executives could trust.

Solution

AWS and Provectus proposed a comprehensive, low-disruption solution that added generative AI (GenAI) to an already operational IoT and ML ecosystem. What made this proposal appealing was its modularity, low-code setup, and ability to work with existing data without major reengineering or data infrastructure changes.

The GenAI prototype targeted the facility’s most failure-prone assets (rotating equipment, such as compressors and cooling fans). This was a strategic initiative to validate how GenAI and machine learning could transform predictive maintenance – by reducing planning cycles, eliminating routine manual inspections, and cutting overall maintenance cost in the refinery operations. From day one, the focus was on quantifiable business value: faster decision-making, lower operational costs, and greater asset reliability.

Our work entailed:

• Real-time sensor data was integrated with ERP systems, enabling automated anomaly detection and maintenance operations, including near-instantaneous permit generation, parts procurement, and workforce allocation.
• Machine learning models, trained using Amazon Lookout for Equipment, analyzed sensor data to detect abnormal operating patterns, enabling early identification of potential failures under asset-specific conditions.
• GenAI tools, particularly large language model agents, were introduced to automate document analysis, generate maintenance summaries, and assist operators in interpreting telemetry data through natural language interfaces.
• Critical to success, GenAI resolved the challenge of limited labeled anomaly data by generating synthetic images of deteriorating equipment (e.g., rust, cracks), effectively creating labeled data that was previously unavailable. This enabled the training of high-performance computer vision models, unlocking reliable structural defect detection where traditional ML approaches had failed.

The prototype’s success validated the business value of GenAI as a non-invasive enhancement layer. As a result, the operator approved a phased rollout across the asset. The demonstrated success of the prototype led to a greater involvement of maintenance and reliability teams, and sparked the exploration of additional use cases beyond rotating equipment.

Outcome

The implementation of GenAI predictive maintenance strategy in refinery operations delivered three significant business outcomes.

• The average work planning cycle was reduced from nearly three months to just a single day by shifting to a fully automated, data-driven workflow
• Inspection costs were cut by about 80%, enabling the client to optimize labor and maintenance budgets
• An increase in processing availability due to more reliable uptime and predictable asset behavior resulted in the operator expanding its role, from processing internal reservoir gas to offering services for new external clients

Next Steps

If your organization faces similar challenges involving delayed maintenance cycles, limited predictive insights, or underused IoT investments, this case demonstrates that you do not need to rebuild your entire system to get measurable and actionable results. Not only did this client avoid costly refinery downtime, they unlocked a whole new revenue stream.

Provectus works with clients through a phased low-risk approach, starting with the discovery phase where we map your current workflows and co-design a solution that builds on your existing system.

Contact us, and together we can identify the best starting point where GenAI can add value to your organization.