Suncor
Suncor optimizes its predictive maintenance with AVEVA Predictive Analytics
Suncor, an integrated global energy company based in Calgary, operates 20,500 critical assets across 14 sites, from oil sands to downstream, including exploration, production and pipelines. Maximizing the availability of this fleet required isolating true degradation signals from normal and ambient fluctuations in indicators. To achieve this, Suncor combined AVEVA Predictive Analytics, AVEVA PI Vision and AVEVA Process Optimization in a predictive maintenance strategy launched in 2017. Dynamic modeling now detects equipment problems up to six weeks before they occur, radically transforming outage planning. The results since deployment are significant: CAD $37 million in savings, a controlled maintenance strategy that manages planned outages, maximized equipment availability and stronger overall profitability for the company.
Testimonials
FAQ
How does AVEVA Predictive Analytics detect failures 6 weeks in advance?
AVEVA Predictive Analytics uses dynamic modeling to separate genuine degradation signals from normal and ambient fluctuations in machine KPIs. At Suncor, this technology continuously analyzes data from 20,500 critical assets across 14 sites and identifies equipment issues up to six weeks before they occur. This early warning turns predictive maintenance into a strategic lever: shutdowns are scheduled at the right time, resources are optimized and costs are kept under control, unlike reactive corrective maintenance.
What role does AVEVA PI Vision play in Suncor's predictive maintenance strategy?
AVEVA PI Vision visualizes real-time performance data from Suncor's turbines and critical equipment. Combined with AVEVA Predictive Analytics and Process Optimization, it gives engineers a contextualized view of machine indicators and makes it easier to identify anomalies early. This real-time data management is essential for running an industrial asset base as vast as Suncor's, from oil sands to downstream, including exploration and production and pipelines, and for democratizing access to operational information.
How much has Suncor saved with AVEVA predictive maintenance?
Since implementing Asset Performance Management (APM) in 2017, Suncor has generated CAD 37 million in savings through the combination of AVEVA Predictive Analytics, PI Vision and Process Optimization. The optimized maintenance strategy makes it possible to control planned shutdowns, improve asset performance and maximize availability. For an integrated energy company, it is a major lever for overall profitability and one more argument for accelerating digital transformation in the oil sector.
How can real degradation signals be isolated from the ambient noise of an industrial fleet?
This is precisely the challenge AVEVA Predictive Analytics solves at Suncor: distinguishing normal, ambient fluctuations in machine KPIs from genuine degradation signatures. Dynamic modeling learns the expected behavior of each piece of equipment in its operating context, then detects statistically significant deviations. This approach avoids both false positives (unnecessary alarms that wear down team vigilance) and false negatives (missed failures), ensuring the operational reliability an integrated operator needs.
Why combine Predictive Analytics, PI Vision and Process Optimization on the same asset base?
Combining these three AVEVA solutions gives Suncor a complete approach: Predictive Analytics anticipates failures, PI Vision makes data accessible and usable by teams, and Process Optimization aligns operating conditions with optimal targets. This native integration covers the entire chain, from detecting a weak signal to taking corrective action on process parameters. This is what sets a true digital transformation strategy apart from a simple stack of isolated tools.