Maybe try slopes and second derivatives (change in temperature over time and so forth) could also try introducing various lag windows into timeseries data. edi

Maybe try slopes and second derivatives (change in temperature over time and so forth) could also try introducing various lag windows into timeseries data. edit: I've Also seen a lot of pitches about predictive maintenance / automated anomaly detection. I think the appeal lies in having a one size fits all solution you can apply to multiple pieces of equipment (fans, conveyor belt drives, pumps etc) and not needing to develop/deploy/maintain bespoke models. A lot of manufacturing sites won't have a data person on tap (or even people who can write python). Also there are challenges with deployment etc especially in remote sites where access is difficult, data connectivity is bad etc (think like oil/gas pipelines). Most of the pitches seem to combine running ML models and using some kind of iot device with something like lorawan for connectivity..