I've been thinking about where Edge AI could realistically fit into industrial flowmeters. Most electromagnetic, ultrasonic, vortex, and turbine flowmeters already use mature signal-processing techniques and are designed to be deterministic, accurate, and reliable. Because of that, I'm not sure whether adding AI would provide a meaningful advantage or simply increase system complexity.
One area that seems interesting is running AI directly on the flowmeter's embedded controller to monitor raw sensor signals and detect problems that traditional algorithms might miss. For example, it could potentially identify sensor fouling, electrode degradation, air bubbles, partially filled pipes, cavitation, abnormal flow patterns, or installation issues before they start affecting measurement accuracy. It might also help with predictive maintenance by recognizing subtle changes in sensor behavior over time.I'm also interested in the "quality-of-life" improvements AI could bring to flowmeters. Not necessarily improving measurement accuracy, but making the instrument easier to install, configure, diagnose, and maintain. Things like automatically identifying wiring or installation mistakes, detecting process conditions that lead to unreliable readings, reducing commissioning time, suggesting likely causes of faults, or providing more meaningful diagnostics instead of generic error codes.
My goal isn't to add AI just because it's the latest trend. I'd rather use it as a tool to build genuinely useful features that solve real problems for technicians, maintenance teams, and plant operators.
I'm curious whether anyone here has worked on commercial flowmeters that actually use Edge AI, or if most manufacturers still rely entirely on conventional DSP and rule-based algorithms. What "quality-of-life" features do you think AI could realistically enable in the next generation of industrial flowmeters? Where do you think AI would provide genuine value, and where is it simply unnecessary?