Predictive Maintenance of Electromechanical Devices with MATLAB and Simulink


One of the most important topics of Industry 4.0 is Predictive Maintenance, which helps companies reduce unexpected breakdowns in their equipment. The sophisticated instrumentation present in industrial machinery allows the collection of an enormous amount of data. The heterogeneous nature of this data makes it difficult to interpret, correlate, and use in a practical way in decision making. Additionally, many companies are missing data from system failures which are critical to establish robust prognostic algorithms.

In this seminar we will show tools to label data, design condition indicators and estimate the remaining useful life (RUL) of an electromechanical device. You can analyze and label machine data imported from local files, cloud storage, and distributed file systems. Creating a digital twin of your machine using multidomain physical networks is critical to simulate and generate synthetic datasets for fault conditions that would be too dangerous and costly to measure during a lab experiment. Using techniques and apps to select and calculate relevant features, a predictive model will be trained from these data to estimate the remaining useful life (RUL) of the device.

Finally, we'll show you how to deploy these algorithms in both enterprise environments (servers, cloud, etc.) and embedded devices using C/C++/IEC61131 code generation.


  • Modeling of electromechanical systems such as batteries, motors, pumps
  • Using Simulink and Simscape to generate synthetic fault data
  • Feature extraction and selection
  • Exploration of a catalogue of machine learning models
  • Remaining useful life (RUL) models
  • Code generation for embedded systems and PLCs
  • Integration in business platforms

Who Should Attend

  • Managers interested in data analytics to improve design and planning
  • Engineers, analysts and data scientists interested in developing analytical and/or predictive maintenance systems
  • IT managers and systems engineers interested in integrating analytics into their business systems.

About the Presenters

Manuel Arias Chao
Chair of Intelligent Maintenance Systems, ETH Zürich

Steve Miller
Steve Miller is responsible for the technical marketing of the Simscape product family at MathWorks. Steve joined MathWorks as an Application Engineer in 2005 and moved to the Design Automation Marketing group in 2006. Prior to that, Steve worked at Delphi Automotive in Braking Control Systems and at MSC.Software Adams consulting in various capacities at Ford, GM, Hyundai, BMW, and Audi. Steve has a B.S. in Mechanical Engineering from Cornell University and an M.S. in Mechanical Engineering from Stanford University.

Patrick Kaufmann
Patrick Kaufmann in his role as Country Manager for MathWorks Switzerland/ Lichtenstein is managing the subsidiary for over 12 years.  Advising leading technology companies on how to design products quicker and with less risk in fast-moving markets coping with mega-trends such as globalization, Digitization and Artificial Intelligence. Prior of joining MathWorks he held several Senior Managing positions at Canon, Xerox and Duap, managing Sales, Operations and Marketing Teams.

Patrick hold’s Dual bachelor’s degrees with a major in Employment Law from GSBA as well Stats and from Lorange Institute of Business in Zürich.

Vasco Lenzi
Vasco Lenzi is a senior application engineer at MathWorks Switzerland. He specializes in design automation with emphasis on multidomain modelling, control design, verification, and deployment. He collaborates with industrial automation and machineries, automotive, and medical companies. Prior to joining MathWorks in 2016, Vasco worked as a development engineer on the modelling and simulation of engines at Liebherr Machines Bulle. Vasco also worked as a control software developer at the Institute of Dynamic Systems and Control, ETH, with active participation in Formula Student competitions. He holds a B.S. in mechanical engineering and an M.S. in energy sciences from ETH Zurich.


Time Title
09:00 Registration
09:30 Digital Transformation: how to do Predictive Maintenance
10:00 Combining Deep Learning and Physics-based Performance Models for Fleet Prognostics
Manuel Arias Chao - Chair of Intelligent Maintenance Systems, ETH Zürich

Getting started with Predictive Maintenance applications

  • Data collection and processing in MATLAB
  • Out-of-memory data handling
  • Interfaces and reference frameworks for IoT and cloud 
11:00 Break

Generate failure data using first principle simulations

  • Multidomain modeling of electromechanical devices with Simscape
  • Parameter estimation and tuning of digital twins
  • Generate datasets for fault conditions

Do not miss this unique opportunity: This presentation will be given by Steve Miller, technical marketing leader of the Simscape product family at MathWorks

12:15 Lunch break and networking

Developing and deploying Predictive Maintenance applications

  • Predictive models for estimating remaining useful life
  • Code generation for embedded systems and PLCs for edge devices
  • Integration in IT/OT infrastructure
14:45 How MathWorks can support you implement Predictive Maintenance applications
15:00 End of the Seminar

Product Focus

Registration closed