4 ms·
But I have also seen people trying to use deep networks to identify rotating machinery faults, like bearings, from raw accelerometer data collected a high frequ
by bsoles 1y ago
But I have also seen people trying to use deep networks to identify rotating machinery faults, like bearings, from raw accelerometer data collected a high frequencies like 40 kHz. Whereas the spectrum of the data from running FFT on the signal contains fault information much more obviously and clearly.
Throwing a deep network on a problem without some physical insight into the problem has also its disadvantages it seems.
- jvanderbot 1y agoYeah, we're shouting into the wind here. I have had people tell me directly that my ideas from old school state estimation were irrelevant in the era of deep learning. They may produce (in this case) worse results, but the long game I'm assured is superior. The specific scenario was estimating the orientation of a stationary semi trailer. An objectively measurable number and it was consistently off by 30 deg, yet I was the jerk for suggesting we move from end to end DL to trad Bar Shalom techniques. That scene isn't for me anymore.
- AnonymousPlanet 1y ago> That scene isn't for me anymore. They will learn. At least when the competition beats their solution with a hybrid approach they can't begin to understand.
- physicsguy 1y agoI’m working on this sort of thing right now in a SaaS product that previously didn’t have support for vibration data. One competitor is all ML-d up to the hilt but customers don’t like the black box and keep finding it gives false positives with no explanation. I think one problem is those buying not understanding problem; they just want to plug in a sensor and insights to happen, but without information about the machine that’s never going to be able to provide useful insights