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Do you work with operational equipment that collects sensor data? In this webinar, you will learn how you can utilise that data for Predictive Maintenance, the intelligent health monitoring of systems to avoid future equipment failure. Rather than following a traditional maintenance timeline, predictive maintenance schedules are determined by analytic algorithms and data from sensors. With predictive maintenance, organisations can identify issues before equipment fails, pinpoint the root cause of the failure, and schedule maintenance as soon as it's needed.
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Feature Extraction Fault Classification
Diagnostic Feature Designer
RUL Estimation
Rainer Mümmler works as a Principal Application Engineer at MathWorks focusing on Data Analytics, Artificial Intelligence, Connectivity to Hardware and on solutions for the Internet of Things. Before joining MathWorks he worked as a wind tunnel test engineer and as a freelancer for various Aerospace companies.
Dean Redelinghuys is the director and Head of Product Architecture at Opti-Num Solutions. Dean has a Masters Degree from University of Witwatersrand, where he also lectured undergraduate and postgraduate level courses on introductory engineering programming, electric circuits, control and system identification theory until 2000.He has been involved in a wide variety of projects in a broad range of industries, including identification and control studies for minerals processing plants, control design and PLC implementation for ferrochrome pilot plants, development of software test beds and simulation models for aerospace and defence systems, and software development projects for automated analysis systems. He has also worked on teams where he has been actively involved in all phases of software engineering for MATLAB and Simulink add-on products marketed and distributed worldwide by MathWorks.