Automation, IIoT, machine learning, artificial intelligence and augmented reality – the next decade promises to be the digital revolution of the industrial world. Our challenge is integrating legacy systems and assets with new technologies to harness the explosion of data to drive new opportunities without risking existing operations. This keynote will cover the challenges of the digital revolution and best practices for establishing an enterprise data infrastructure to support your digital transformation as you invest in edge, enterprise and cloud technologies.

We are being asked to run equipment longer, faster, and harder with less resources while minimizing failures. Condition based maintenance (CBM) takes a data driven approach to maximizing the performance of your assets and maintenance schedules.  Discover how the PI System can be used for CBM through simple drag and drop configuration. Configure digital representations of your assets, calculate key performance indicators (KPIs), and even send notifications or kick off maintenance work flows based on the real condition of your assets.  Jump-starting your CBM project has never been easier with the modern PI System.

Big data, data lakes, machine learning and artificial intelligence are offering the allure of new insights and answers to efficiencies.  But sometimes the first big win comes from a little improvement using the data your already own.  Learn how you can break down seemingly complex big data problems into a manageable sequence of steps with the PI System.  Even without waiting for completion of a full-blown statistical model, your operational experts can often spot anomalous patterns with little more than a few events captured as PI System event frames and displayed in a report.  And when you are ready to push data to advanced machine learning (ML) tools, the PI Integrator line is available to jump-start your data science initiative with clean, ready-to-consume, reliable data. 

We will share lessons learned of various types of analytics applied to the oil and gas industry. As the O&G Industry continues to have pricing pressures placed on it, the application of analytics is providing optimization, increased capacity throughput and subsequent financial impact applying analytic methods. Financial results are the greatest in complex, capital intensive process facilities like those found in the downstream area of the Oil and Gas Industry. We will share upstream and midstream analytics and what analytics have worked well, we will share specific examples including analysis using shape files, used to determine specific predictive analytics ; machine analytics, applied to turbines and compression and predictive analytics applied to various systems to allow predictive learning, automated generation of notifications and work orders. Customer examples will be shared of the value Oil & Gas companies have achieved including optimization, higher capacity in operating assets and greater ROI with analytics.

A picture is worth a thousand words.  That's why executives and managers around the world rely on visual reports and dashboards to stay in touch with the health of their businesses.  Generating these reports is often a tedious, time-consuming task that repeats every month, quarter, or year.  In this session, you’ll see how you can quickly access disparate data coming from both legacy and new IoT sources to deliver smart, real-time dashboards that drive visibility across the enterprise.  And that monthly compliance report?  The PI System allows you to easily refresh reports and be alerted to compliance deviations before they become an issue.

• How to use templates to start creating AF elements and attributes across assets
• How to achieve a well-designed structure, plan for the usefulness of the AF model
• How to use categories to organize large numbers of elements and attributes
• How to set explicit Unit of Measures (UOMs)
• How to use inherited templates to allow flexibility while maintaining standardization
• How to build different views for your users using elements references
• How to fine-tune the asset model using attributes, hierarchies, enumeration sets and data references