Using Multi-Domain Digital Twins to Develop Fault Classification Algorithms
| Start Time | End Time |
|---|---|
| 7 Oct 2026, 04:00 EDT | 7 Oct 2026, 05:00 EDT |
Overview
This webinar demonstrates how a multi-domain digital twin can be used to simulate machine faults, generate synthetic fault data, extract diagnostic features, and develop fault detection algorithms. Using a flow pack machine example, attendees will see how mechanical and electrical fault scenarios can be modeled and used throughout the algorithm-development process.
Attendees will learn how synthetic fault data can support representative test cases, feature engineering, and supervised models that classify known operating and fault conditions.
Attendees will leave with a practical workflow for translating physical system knowledge into validated fault detection algorithms for condition monitoring and predictive maintenance applications.
In the following webinar of this series, attendees will see a simulation-based workflow for validating, deploying on industrial controllers, and verifying fault detection algorithms.
Who Should Attend
This webinar is intended for automation and controls engineers, data scientists, and technical leads working on fault detection, condition monitoring, predictive maintenance, and industrial AI applications.
About the Presenters
Martin Förster | Application Engineer at MathWorks
Martin supports customers working with model-based design, focusing on PLC code generation and real-time simulation. Previously, as Application Engineering team lead at machineering GmbH & Co. KG, he guided machinery and plant manufacturers in virtual commissioning projects. He holds a master's degree in electrical engineering with a focus on automation technology.
Dmitry Samarkanov | Senior Application Engineer at MathWorks
Dmitry helps organizations assess and deploy MATLAB and Simulink applications from desktop through to cloud platforms. Dmitry has more than 10 years of hands-on experience developing cloud native solutions. Prior to joining MathWorks, he architected and deployed web-based cloud applications on to highly complex software systems at ASML. Dmitry holds a Ph.D. in Electrical Engineering and a master’s degree in Business Intelligence.
Rareș Curatu | Industry Manager at MathWorks
Rareș is responsible for Industrial Automation and Machinery business development. He works with equipment manufacturers and industrial robotics teams on model‑based engineering workflows, including simulation‑based validation, virtual commissioning, and the application of data‑driven and AI‑based techniques in control, monitoring, and system‑level behavior of industrial machines and robotic systems.
Product Focus
This event is part of a series of related topics. View the full list of events in this series.
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