How Machine Learning Can Help with Predictive Maintenance for Industrial Applications

Predictive maintenance is a data- and technology-based method of carrying out proactive maintenance. The main goal of predictive maintenance is to identify potential machine issues well before they lead to critical situations with shutdowns. This is realized through constant equipment performance monitoring enabled by sensors, data collection, and near-real-time communication between equipment and software. The new rise of interesting automation equipment & parts enable this in part or can at least run C++, which is of great help in such a context. Predictive maintenance can offer numerous, unparalleled benefits in productivity and efficiency — benefits which you will be able to understand from this predictive maintenance session between RealPars and Edge Impulse. It is also important to understand predictive maintenance challenges exist, but are well worth the future ROI of an edge ML enabled predictive maintenance solution.

Learn more : https://edgeimpulse.com/realpars-edge...

Speakers:
Shahpour Shapournia: Co-founder & CEO, RealPars
Ken Bourke (BEng): Senior Automation & Controls Engineer, RealPars
Dr. Jan Jenke: Product/Project Manager, Analytics, WAGO
Mihajlo Raljic: Director, Sales & Business Development EMEA, Edge Impulse
Louis Moreau: Senior DevRel Engineer, Edge Impulse