What is the most critical data to measure for a predictive maintenance model for a drone? I'm looking at making a predictive maintenance model for a drone (quadcopter mainly but can include others)

I'm wondering what is the most common causes of failure due to "overflying". (not including crashes etc) and how I would quantitatively measure it.

eg. I've read that one mode of failure is rotor malfunction is one of the most common causes of faults and so I would therefore use a vibration sensor (positioned near the rotor?) to measure it.

Are there other common failure modes? And what data can we collect from a drone that would help mitigate those?

Any help much appreciated!!

Kindest regards

Jonathan
Predictive Maintenance (PM) typically relies on a triad of vibration, thermal and oil analysis, or any combination thereof. Probably vib and thermal would apply. Cheapest option would be to physically hold the drone while running (if possible) and acquire vib and thermal, using handheld instruments. All-in option would be a hardwired system, permanently installed. With a hardwired system, you'll need a vib and a thermal transducer installed on each power/rotor module. Hope this helps.