In any business, a machine failure can cause headaches for you and your customers, cost big money and even pose a hazard to life and property. In oil and gas, with its large scale and volatile materials, the risks intensify. Our parent company, SAP, just announced a new approach to heading off trouble before it happens. Aimed at oil and gas equipment, it uses big data to diagnose problems faster and better and “shows potential for extending machine life, avoiding failures, and saving money in a high risk industry” according to SAP.
It comes out of the IoT (“Internet of Things”) kickstarter program at the SAP Co-Innovation Lab in Silicon Valley, is a partnership with Mtell and Rolta, providing predictive machine maintenance for oil and gas industry equipment. David Cruikshank, senior director of the SAP Co-innovation Lab, told SAP News: “The solution automatically sends an alert on potential machine failure, expected time-to-failure, and provides prescriptive advice. This makes it possible to fix small problems before they become big ones. Ultimately, this solution enables early action to avoid or reduce the effects of machine degradation and failure.”
How it works
Cruikshank explained how the partners’ systems work together here: “Mtell created a machine learning and neural network that recognizes patterns associated with machine performance. Their software automatically generates notifications and sends them to SAP Plant Maintenance, which planners use to schedule machine repair and replacement. We improved their ability to analyze volumes of O&G asset data by integrating the solution with SAP HANA. The in-memory technology and analytics of SAP HANA enable real-time analysis to pinpoint potential machine failures.
This analysis is then delivered to Rolta’s OneView software solution, where it is displayed on one of the 30+ job-specific dashboards designed for O&G operations personnel. Rolta’s solution is available for SAP BusinessObjects business intelligence and the SAP HANA platform, so customers can choose whatever environment is best for them.” In effect, the machine tells the system how it’s running; the system interprets and analyzes that and warns operators before there’s a big failure.
Where it stands right now
Cruikshank says: “This is the first IoT kickstarter project at SAP Co-innovation Lab in Silicon Valley. It was a pilot and was completed in just over 90 days. A whitepaper and new demo will be released soon. A follow-on project is underway for complete delivery of the solution on the SAP HANA Cloud Platform.”
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