AI-Based Fault Detection on Industrial Controllers

Overview

In this webinar, we explore the integration of predictive maintenance (PdM) systems with industrial controllers — a pivotal development in Industry 4.0. Discover how Siemens automation systems and Model-Based Design can optimize industrial operations. We will explore the integration of Simulink® components within the TIA Portal and LiveTwin, demonstrating the practical benefits with a live demo of fault detection using Siemens automation technology. The demo will showcase synthetic data generation via a digital twin approach with a focus on an industrial fan application. The project aims to improve fault classification in industrial equipment by incorporating advanced PdM strategies into the Siemens framework. MATLAB® and Simulink® offer deployment strategies that have been optimized to provide significant operational advantages and AI-driven applications to Siemens customers.

The webinar will illustrate a PdM framework for Siemens PLCs and edge devices by simulating faults in a physics-based model to create datasets. This will be followed by data preprocessing and feature extraction. A machine learning model is then developed for fault classification that is tailored for edge computing. Integration with Siemens hardware ensures the efficient deployment of PdM models for real-time monitoring and swift issue resolution, thereby minimizing downtime and advancing smart manufacturing.

Highlights

• Synthetic data generation via a digital twin approach
• Data preprocessing and feature extraction
• Fault classification Modelling tailored for edge computing
• Efficient deployment of PdM models for real-time monitoring

About the Presenters

Conrado Ramirez Garcia
Senior Application Engineer | MathWorks 

Conrado is an application engineer at MathWorks Germany. He specialized in supporting customers of different areas including machine builders, industrial robotics, wind turbines, automation, and energy production. His focus area includes code generation for embedded devices and industrial controllers.

Before joining MathWorks, he worked for 5 years at GE Aviation and GE Power as a Controls Engineer. Conrado holds a master’s degree on Power Engineering from the Technische Universität München, where he specialized on Control Systems, Energy Systems and Power Electronics.

Rainer Mümmler
Principal Application Engineer | MathWorks 

Raineris a Principal Application Engineer at MathWorks, specializing in Data Analytics, Artificial Intelligence, Predictive Maintenance, Hardware Connectivity, and IoT solutions. Prior to his tenure at MathWorks, he gained valuable experience as a wind tunnel test engineer and served as a freelance consultant for multiple aerospace companies.