3 ms·

There is potential for integrating ML with time series data in industrial applications (things like smelters, reactors etc.), where you have continuous stream o

There is potential for integrating ML with time series data in industrial applications (things like smelters, reactors etc.), where you have continuous stream of time series measurements from things like gauges and thermocouples. If you can detect (and respond) to changing circumstances faster then a humans in control room reacting to trends or alarms then potential big efficiency gains... Operator guidance is often based on heuristics - when metric A exceeds X value for Y seconds take action Z. Or rates of change if the signal is changing at a rate of more than x etc. So in these areas there exists potential for ML solution, especially if it's capable of learning (i.e. last response overshot by X so trim next response appropriately).

kqr 2y ago
Every time i've actually tried something like this it has not outperformed statistical process control. It's not just that control charts are great signal detectors, but also managing processes like that takes a certain statistical literacy one gets from applying SPC faithfully for a while, and does not get from tossing ML onto it and crossing fingers.
- chaos_emergent 2y ago
  > Every time i've actually tried something like this it has not outperformed statistical process control. There are clear counterexamples to your experience, most notably in maintaining plasma stability in tokamak reactors: https://www.nature.com/articles/s41586-021-04301-9 https://www.nature.com/articles/s41586-021-04301-9
  - whimsicalism 2y ago
    task specific model
  - kqr 2y ago
    Interesting. Could you point me to where it is compared against SPC? I didn't find it from a cursory read.