Anti-Hackathon - Securing a PI System - In this lab you will be served a big, soggy mess of a PI system – it’s your job to whip it into shape, by applying modern security techniques and best practices. You will have some help - handy scripts to identify the security holes are, references, resources, tips and coaching to help you accomplish your task. Participants will earn points based on the amount and the severity of security issues addressed. At the end of the lab, prizes will be awarded to top scorers. Moderately experienced administrators may have an advantage, but participants at all experience levels will learn concepts applicable to their systems back home.
Apply Predictive Machine Learning Models to Operations - The latest release of the PI Integrator for Business Analytics (BA) includes new functionality for streaming PI data into predictive analytics tools like Spark or TensorFlow. In this lab, we will leverage the capabilities of the PI Integrator for BA to develop and train a predictive model from archived PI data. We will then operationalize it by streaming PI data to the model providing real time predictions which can be posted in the PI System for operational relevance. A familiarity of PI AF structure and knowledge of Python are prerequisites for this session.
Utilizing Event Frames for Plant Operations and OEE Analysis - This lab teaches how to utilize Event Frames to monitor plant operations effectively, as well as for Overall Equipment Effectiveness (OEE) analysis. The exercises in this lab focus on using Event Frames to monitor plant-wide health of equipment through the PI System: PI Vision, PI DataLink, Asset Analytics, PI Builder, and PI System Management Tools. Topics of discussion in this lab include: what real-time events should be captured using Event Frames, how to delineate important events from average events, how best to visualize Event Frames, and how to use Event Frames to increase the efficiency of plant-wide equipment.
Timesaving Tips while Deploying AF -
You have already attended several sessions and heard about the power of Asset Framework (AF). You are eager to implement AF or even to expand the existing AF structure in your organization, but… not sure where to start from. Are you wondering if there are any timesaving tips for deploying scalable and consistent AF structures?
In this session, you will build a simple AF structure from ground-up using the recommendations and guidelines:
· 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 AF 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 element references
· How to fine-tune the asset model using attributes hierarchies, enumeration sets and data references
Building Displays with PI Vision - Discover why PI Vision is truly the next generation of PI System visualization. Create, format and organize your displays with graphics, multi-state values, and trends. Group symbols and create pinned event for comparison purposes. Explore the interaction of AF and PI VIsion for events. (Beginners)
Fit for Purpose - Layers of Analytics using the PI System – AF, MATLAB, Machine Learning - This hands-on lab covers scenarios to illustrate the different levels of analytics that are fit-for-purpose when using the PI System. For example, what calculations and analyses do you do in AF? When do you use MATLAB and similar libraries for advanced calculations that hook into AF? When do you call on “data science and machine learning ?” Use cases will include those focused on an equipment, such as a pump, motor or compressor, and those focused on a process. We will also cover examples of advanced analytics that are part of the data collection from the edge devices and contrast these with predictive analytics – both the model development and model deployment (scoring new incoming real-time data) and include cloud vs. fog vs. edge scenarios.
Develop a PI JDBC Project to Exchange Data with PI Raspberry Gadgets - Work with our latest platform independent PI JDBC driver, install it on a Raspberry PI and develop a Java applet. The Lab will guide you on how to interact from Java with sensors and actuators and exchange data with the PI System via PI JDBC.
Advanced Analytics for PI Data for Data Scientists - PI System is the standard time series data infrastructure across several industries. Decades of operational data collection has turned each PI System into a treasure trove for data scientists to extract knowledge. Come to this lab and learn, as a data scientist, how you can access, shape, and use PI data to generate insight and knowledge. A basic understanding of data science basics is required for this lab. No knowledge of PI System is necessary.
Getting Started with PI Web API using Postman - How do you programmatically access PI data? Traditionally, this has meant installing an OSIsoft library or driver and then introducing your own solution. In order to improve deployment and development experience for newer and mobile environments OSIsoft offers a RESTful API called PI Web API. Come learn how to make your first requests against PI Web API, see how it can free up your development and give you new ways to bridge the PI System with the rest of your enterprise. This lab is intended for those with a programmatic mind who have a basic PI System knowledge. No knowledge of any particular programing language is expected.