Improving uptime in industrial robotic systems: Page 5 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.

The XMC4000 family features a powerful ARM Cortex-M4F processor at their core, enabling the implementation of sensor fusion applications. The processor’s support for floating-point calculations and digital signal processing will also be beneficial in processing and evaluating the sensor data. Compact packaging, such as low-profile VQFN and LFBGA, ensure that both sensing and data processing can be simply integrated at the point of measurement.


Fig. 6: Sensor data can be fused together using
a microcontroller, such as the XMC4000 series,
that feature industrial interfaces including EtherCAT.

With manufacturing facilities operating 24/7, such sensor fusion systems will be generating a lot of data. With intense scrutiny, it is possible that human operators could determine anomalies in some of the data collected. But with robots handling a multitude of loads and parts, it will be challenging to determine if, for example, differences in current consumption are purely load related or indicative of an impending breakdown.

 

Can artificial intelligence provide new insights?

This is where the essence of the experienced maintenance engineer needs to be replicated. It is the combination of overlaid data, such as knowledge of load, sound, vibration, warming and motion that allows them to sense a pending failure. This falls squarely into the domain of artificial intelligence (AI) analysis techniques.


Fig. 7: To make sense of the huge quantities of data generated, subtle changes over time could be detected using artificial intelligence techniques.

AI is all about pattern recognition, often across data sets of dissimilar sources. Through analysis of data captured in the time domain, coupled with knowledge of the task being undertaken, a known-good state of health could be determined. AI would then be tasked to find anomalies in sound or vibration that correlate with increased current consumption or a rise in temperature – changes that could be indicative of a pending breakdown.

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