Industrial systems are becoming increasingly “smart” with the integration of sensors into various equipment and manufacturing processes. Smart industry system designs and implementations span a range of use cases and challenges. For example, industrial equipment makers face complex challenges in developing and maintaining embedded applications. On the other hand, manufacturing equipment users are challenged by how to use sensor data to improve the efficiency and uptimes of their processes. In these instances, developing applications for monitoring and predicting plant operations can be complex and controlling the plants can be difficult because testing the applications in actual plants can be expensive and dangerous. Forward-looking companies are turning to MATLAB and Simulink to solve these challenges. In this seminar, you will hear about how to leverage MATLAB and Simulink for your predictive maintenance and control system design applications.
|13:00 – 13:45||
Digital Twins and Predictive Maintenance with MATLAB and Simulink
Do you need to move to predictive maintenance but are having trouble with your data? Not enough failure data to correctly train your models? What about scaling out for production? This talk will cover an overview of how MATLAB and Simulink can combine with your existing data, directly talk to your PLCs and historians to train predictive maintenance models. But with MathWorks you can also close the loop by pushing, at the click of a button, your models back out to your production control systems and edge devices.
|13:45 – 14:30||
Virtual Commission with Model-Based Design
Virtual commissioning uses dynamic models to design and validate algorithms for improved productivity. After validation, these algorithms can be automatically deployed to a simulated PLC, where the code running on the PLC is tested against the plant model.
|14:30 – 15:00||
Spinning Brushless Motors with Simulink (Optional)
Brushless motors rely on field-oriented control to regulate currents to motor windings. In this session, we will look at some of the challenges and solutions for developing field-oriented control algorithms to achieve the 10-20KHz switching frequency required for efficient and accurate motor operation.