Improving uptime in industrial robotic systems: Page 6 of 6

October 05, 2018 // By Clemens Müller
Machine maintenance can sometimes seem like a black arts. A seemingly reliable machine suddenly stops working and, despite everyone’s best efforts, cannot be brought back to life. Along comes a member of the maintenance team and, after some beard scratching, tinkering inside the machine and a few words of encouragement, the machine springs back to life.

Further justification for Industry 4.0 investment

Such insights could lead considerable improvements in predictive maintenance, allowing teams to undertake precision servicing on specific machinery or robot joints, reducing downtime to the minimum required. It is also conceivable that this information could be used to pro-actively engage an emergency mode. This could involve slowing a robot’s motion down or reducing loading to maintain production until the next suitable moment for repairs arise.

 

With a multitude of compact, low-cost and highly integrated sensors available on the market, there is little reason not to consider implementing self-monitoring capabilities on industrial robots and automation systems. High-performance microcontrollers are also easy to integrate, providing a sensor fusion function to draw the many strands of data together, linked against a common time base.

Investments in an Industry 4.0 implementation, or upgrades of existing equipment to these standards, are easier to justify if there are more benefits through doing so. And with advances in AI, the flood of associated data generated by this approach will be easy to stem and analyze. If these increases in uptime and more efficient, data-based, predictive maintenance can be achieved, there are significant business margin and profitability opportunities available for those that implement them.

 

About the author:

Clemens Müller is Director Business Development Industrial Robotics at Infineon Technologies - www.infineon.com

 

 

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