Barrick Tomcat - Created by Atomic 79 for UC 2017 Hackathon

Version 2

    For this year's hackathon, we worked with data from Barrick Gold Corporation to create an amazing application suite, leveraging the power of OSIsoft software in conjunction with other complementary technologies.


    The Atomic 79 Team

    The atomic number for gold is 79, and our team is all about leveraging the OSIsoft ecosystem and other complementary technologies to maximize the production of gold.  Our team members were Mina Andrawos, Stew Bernhardt, Seth Gregg, and Dave Johnson.


    Introducing Barrick Tomcat

    We created the Barrick Tomcat  (Truck Operation Maximization Created with Advanced Technology) application suite.  Tomcat enables Barrick to maximize the uptime of their trucks through several unique, cutting edge technology components.  All of this is made possible through a solid data foundation that leverages PI AF to rank overall truck health status which takes into account different factors based on a number of sensors associated with each haul truck.



    Our Barrick Tomcat application suite includes the following components:


    Tomcat LEDs with Ease

    This provides the data visualization of truck health status through a color LED display with one LED per truck.  The technology includes a Raspberry PI (or laptop) that utilizes Node.js to retrieve the truck health status from PI Web API and light up the appropriate LED on an LED trip using an Arduino.  The LED color status indicators are as follows:

    •   green - good
    •   yellow - warning
    •   magenta - severe
    •   red - critical
    •   blank - no data


    Here is a photo of the LEDS in action:



    Tomcat Bot on the Spot

    • Leverages NLP (natural language processing to obtain the status of various truck components using an intuitive chat-based interface
    • For example, “Payload of truck 403”
    • Uses the Microsoft Bot Framework in conjunction with PI AF



    Tomcat Speech within Reach

    • Speech to Text Driver Annotations
    • Converts human voice to PI tag values
    • Enables drivers to annotate their observations of road conditions and truck conditions in real-time
    • Text is stored in AF attributes as text-based annotations to provide additional context to the sensor data to aid in data analysis


    Please see the attached PowerPoint presentation for additional details.