Cognitive Analytics and Next-Gen Prognostics for the Industrial Internet

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1 Cognitive Analytics and Next-Gen Prognostics for the Industrial Internet Usman Shuja Sumant

2 Focused on large growing markets SparkCognition is targeting a $10B Internet of Things Security market Predictive maintenance within the Industrial Internet is a sizeable market IoT Security Software Market in Billions ($) Machine Condition Monitoring Market in Billions ($) +8% +6% Estimate of number of connected devices in billions $240B is lost in the US due to bearing failures $150B in waste across major industries that the Industrial Internet can eliminate Source: IDC, Gartner and SV Biz Journal; 30% SW of the tech market. 16% of this Security, per current Global SW: Sec SW ratio; Machine Condition Monitoring market IBIS and Global Strategic Business 5

3 Proven Use Case: Industrial Internet Value Addition Sophisticated failure prediction extended forewarning (from hours to days) Insights Client - Top-5 Power company in the US Decisions on replacing expensive capital assets (Six large boiler feed pumps) Solution Asset-agnostic prediction to cost-effectively support a large fleet of diverse assets Automated model building to augment and expand the capabilities of human data scientists Automated model tuning over time to adapt to changes in operating conditions and environment Automated anomaly identification in historic data Powerful analytics, visualization and alerting 34

4 Client Case Study

5 Business Problem and Current Limitations Business Problem Flowserve, world-leading supplier of industrial and environmental machinery such as pumps, valves sought cognitive technologies for better failure detection, forewarning and maintenance insight for its customers Limitations Current signature library based approaches, and threshold systems could only identify failures a few hours prior to them occurring Additional issues with the current approach Insufficient time to respond effectively Hard to maintain prognostic models Inability of prognostic models to adapt to unique conditions of each individual pump 5

6 Results Identify operating modes, provide advance warning of failures Application 1 Application 2 Objective Recognize known operating modes Detect anomalies with wide variations Advanced warning of possible problem earlier than what simple threshold detection methods provide Results >99% accuracy in identifying desired operating modes Application meets the four criteria set by Flowserve Potential failures predicted 5 to 6 days in advance Minimal false positives 6

7 Valuable Insights Asset State Fleet State Is there a problem? If so, what kind of problem? Is there a problem we ve never seen before? (signature DB approaches don t work well here) Is the entire system operating well? Is the entire fleet optimized? Failure Prediction When will the problem occur? What problem will likely occur next? Forensics What factors were most responsible for a failure? What factors were most responsible for a sub-optimal state? 7

8 Other Cognitive Analytics Applications

9 Proven Use Case for Energy Improve safety and reduce remediation cost through intelligent prognostics Next Generation Analytics & Prognostics Accurate failure prediction and anomaly detection Automated model building, selection & management Insights through deeper-order analyses Flexible and scalable architecture In-context technical advisory with IBM Watson 9

10 Proven Use Case for Security Many potential vectors of attack even in an air-gapped facility Potential vectors for traditional security approaches 10

11 Potential Use Cases for O&G Improve efficiency and reduce failures Goal Decisions 1 2 Improve well efficiency by reducing stuck pipe Improve well efficiency by improving ROP (rate of penetration) Recommend design based on formation type, well trajectory such as type & placement of stabilizer in BHA Predict requirements for hole clean outs/ wiper trips Optimize drilling & operating parameters such as mud weight, mud type & maximum connection time Recommend BHA design such as Bit type, Downhole motor type based on formation Optimize drilling parameters such as Weight on Bit, torque, RPM 3 Reduce failures (downtime) Predict when downhole equipment (e.g. mod motor) might fail 11

12 Contacts Usman Shuja, VP Market Development Sumant Kawale, Sr. Director Business Development 6034 W. Courtyard Drive, Suite 100 Austin TX 78730