Energy Firms Amp Output with Analytics
Equipment failures aren’t merely costly – they can be dangerous for employees and disastrous for the environment. Predictive analytics from business analytics firm SAS helps aircraft maintenance providers, manufacturers, energy companies and more use big data collected from machines and equipment to predict issues before they cause brutal disruptions.
SASPredictive Asset Maintenance helps companies like POSCO and Shell Exploration and Production Co. avoid unplanned downtime and keep production goals on target. With SAS Visual Analytics, companies can analyze data to find systemic root causes, ensure higher safety and protect production commitments.
Industry experts identify unscheduled downtime as one of the biggest challenges facing energy processing plants. The ARC Advisory Group says unscheduled shutdowns and slowdowns account for as much as 7 percent of lost production. The U.S. Department of Energy estimates that a functional predictive maintenance program could create a tenfold return on investment, increasing production up to 25 percent.
“Competitive pressure and increasingly tight regulations require analytical solutions that exceed traditional asset management systems,” said Reinhard Hoene, SAS senior product manager. “Organizations that couple predictive analytics with visual analytics essentially have a data ‘GPS’ helping them find the critical information in their mountain of data. Being able to see performance degradations early and take preventive measures before an issue becomes a costly or dangerous problem – that is priceless.”
For large-scale projects such as massive defense contracts, a critical lack of predictive analytics is almost like flying blind.
“Operational disruptions and catastrophic accidents often happen when maintenance procedures aren’t followed reliably,” said Andrew Hess, president of the PHM Society and former lead for prognostics and health management (PHM) on the U.S. Department of Defense’s F-35 Joint Strike Fighter Program. “When you can’t accurately predict impending failures and estimate useful life remaining, you risk significant reduction in system availability.”
Predictive Asset Maintenance can help a company move from a reactive “What’s happening?” to a predictive “What needs service or replacement now to keep production humming during the next service cycle?” Recent upgrades offer an expanded data model, greater data selection options and a flexible framework that supports various industries.
The software combines data integration, visualization, descriptive and predictive analytics, and business intelligence to create an unbiased, big-picture view of asset performance. These capabilities increase uptime while optimizing maintenance costs and asset life cycles by predicting events that cause outages. Future asset or process failures are easier to solve because prior mitigation efforts are recorded in a centralized knowledge repository, facilitating speedy root cause analysis.
Visual Analytics is a high-performance, in-memory solution designed to quickly explore large amounts of data. Users can spot patterns, identify opportunities for further analysis and convey visual results via Web reports or iPadand Android tablets.
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