Learn advanced techniques for machine data logging and visualization in this 5-day course. This course teaches you to use Node-Red to store time-stamped process data in on-premises or cloud databases and display real-time and historical data for remote monitoring and data-driven decisions.
The Level 2 & 3 Machine Data Logging & Visualization for Smart Factory program is a 5-day advanced course tailored for manufacturing production teams who have completed the Data Generation program. This course focuses on:
Machine Data Logging and Visualization: Learn methods for logging and visualizing machine data, essential for monitoring manufacturing processes.
Utilizing Node-Red: Gain practical skills in using Node-Red to log machine process information and sensor data with time-stamp details. Data can be stored in an on-premises or cloud-based database (such as SQL or time-series).
Real-Time & Historical Data Display: Use a time-series visualization platform to access real-time and historical data, enabling remote monitoring and insights for improved decision-making.
For manufacturing production teams with good knowledge on Data Generation looking into advanced data storage and visualization to allow remote access of real time & summary data, for analytics and informed business decisions.
Successful completion of SHRDC Data Generation course.
5 Days
Participants are exposed to theoretical fundamentals and demonstrations of information technology related to smart factory competencies and processes, followed by hands-on activities to support application of competencies acquired.
Able to apply the knowledge, techniques, and tools to log data generated from a machine for monitoring and visualization
Able to log machine process and sensor data with time-stamp information into on-premises and/or cloud-based database (such as SQL and/or time-series database) using the flow-based development tool, Node-Red
Able to display real-time and historical data from an on-premises and/or cloud-based database using a time-series visualization platform
