The road to prescriptive maintenance

How industry leaders in energy, oil and gas, and more are mapping out a smarter future for maintenance.

By Sheila Kennedy, CMRP, contributing editor

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The PI System provides the real-time operational data infrastructure and configurable, streaming analytical platform for MOL’s refining division. Predictive and condition-based maintenance, data aggregation, and health scoring is done in the PI Asset Framework (PI AF) and sent to SAP PM, which generates the work orders.

MOL is using a “layers of analytics approach,” with human analytics and real-time/streaming analytics providing a foundation for higher-level, operationally focused ML/AI, explains Craig Harclerode, global industry principal for O&G/Petrochem at OSIsoft. MOL built momentum and awareness of the power of analytics by asking the operations managers what problems needed to be solved and then quickly solving them.

“Once they had an analytical foundation, they moved to identifying areas where more-advanced prescriptive and predictive analytics would have value and began developing ML applications accordingly,” Harclerode says, noting that MOL currently has more than 25 ML-based applications in production.

This approach works because, as Bereznai explains, IT/OT transformation is a long journey that involves not only architectural and analytical method changes but also multilevel synergies among people and processes.

“This is a really long journey, especially in terms of mindset change and cultural development,” Bereznai says. “The technology and software side is much easier to change than the mindset, and the impact of this is underestimated.”

The efforts are paying off. MOL’s digital and downstream business transformation has delivered $1 billion in its first four years, and the goal for the next two-year period (2017-2018) is an additional $500M in EBITDA.

Prescriptive performance analytics for Tata Power

Software companies such as AVEVA are working quickly to answer the call for RxM. “We are building prescriptive maintenance and analytic capabilities into all of our asset performance management solutions to help our customers optimize the entire asset lifecycle and to ensure they have access to the most advanced technology available,” says Sean Gregerson, global director of asset performance management sales at AVEVA.

Tata Power, one of the largest integrated power companies in India, has rolled out AVEVA’s Predictive Asset Analytics software to 10 units at three plants to enhance the reliability of its critical-power plant equipment. The rollout is putting Tata Power in a position to quickly incorporate RxM capabilities.

The utility set its sights on remote, fleetwide continuous monitoring and diagnostics of critical asset health and performance in 2014 with the goal of improving efficiency, enabling proactive maintenance, and avoiding unplanned downtime. It built a new Advanced center for Diagnostics and Reliability Enhancement (ADoRE) powered by Predictive Asset Analytics.

The software learns an asset’s unique operating profile during all loading, ambient, and operational process conditions. When existing machinery sensor data is compared with real-time operating data, subtle deviations are revealed. Alerts and fault diagnostics are generated and plant personnel are dispatched quickly to take corrective action.

One recent catch yielded an estimated $270,000 (U.S.) in cost savings. Analytics revealed that the top thrust and guide bearing temperatures of some circulation water pumps were exceeding expected levels. During a brief planned outage, clogging in the bearing-cooling water line was identified and cleared, thus normalizing subsequent operation.

“Tata Power demonstrates the power of using analytics to move away from a reactive maintenance strategy,” AVEVA’s Gregerson says. “By catching problems early using APR and ML, the company was able to reduce maintenance costs, minimize unscheduled downtime, and prevent equipment failures.”

Prescriptive scheduling for Devon Energy

Prescriptive approaches can be simple to introduce incrementally. Devon Energy has thousands of batteries of tanks that collect water and oil during the course of operations, and how that liquid is scheduled for haul-off has recently become prescriptive. Real-time data engineer Don Morrison described the transition in a presentation at the ARC Industry Forum in Orlando in February.

Previously, scheduling liquid tank haul-offs for the Oklahoma City-based independent oil and gas company involved collecting data from multiple parties in an Excel spreadsheet and then using that file to create schedules. A centralized, more-accurate, on-demand process was needed to prescribe when, where, and how haul-offs would be needed.

Morrison explained: “We already had SCADA systems monitoring oil and water tank levels, so why not use them to detect when haul-off trucks are on site and how many; whether water or oil is removed from the tanks and how much – we only want full loads – and the fill rate?”

Two specific answers were sought: Could the engineers predict when the next load needed to occur so they could schedule the right number of trucks 3–4 days out? Could they gain enough data to “grade” their service providers?

Devon Energy chose Seeq analytics software to quickly detect haul-off events based on real-time OSIsoft PI data. With the push of a “get loads” button, all of the data from PI are pulled; forecasts up to three days out are generated; and the spreadsheet gets filled automatically. The results are reported in Microsoft Power BI, where they can be sliced and diced as needed.

Excel was retained in the first stage because “we didn’t want to change everything the users were doing and they were comfortable using it,” Morrison explained. Other future goals for Devon Energy include auditing and grading haul-off vendor performance and potentially incorporating opportunities such as RxM, smart contracts, and blockchain.

As more companies like these advance to prescriptive analytics and RxM, prescriptive maintenance has the potential to further heighten visibility and respect for the maintenance profession and its positive impact on the bottom line.

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