Webinar Description

Perhaps you have been wondering what the real difference is when it comes to high-end and low-end condition monitoring solutions. The price-factor is clear, but what about performance?

Traditionally, the data produced by your condition monitoring system is used purely for understanding the health of your machines and planning maintenance accordingly. Patterns belonging to the process itself, for example signals from knives in wood chipping or lifters in ore mills, are often disregarded. However, when the condition data is of high quality, these process-related signals might tell you something about the efficiency of the process itself – information which may be used in machine learning and process optimization. High-quality data produced by high-end solutions will also give you lower risks and higher flexibility in planning your maintenance, an absolute requirement for higher-criticality machines.

 Learning Takeaways

* Learn how you can use condition monitoring data in machine learning and process optimization. Using real cases from the industry, Tim will guide you through real applications in the industry.

* Understand machine criticality, when to choose low-performing systems and when high-performing systems are an absolute requirement.

* Get an understanding of risk management, and how crystal-clear data gives you increased planning horizons and opportunities to increase the distance between, or completely remove, certain planned maintenance stops.

About the Presenter

For over twenty years, Tim has been specializing in electronics development and has held managerial positions in the field since 1992. In 2001, he joined SPM Instrument as head of Research and Development, where he has been deeply involved in HD Technology development and field evaluations. Today Tim is head of the Strategic Sales and Applications expert group at SPM Instrument, focusing on developing key areas and knowledge.