Rare event prediction on time series that change structure mid-stream? [D]

Hi reddit! I made this post on r/MLQuestions, but I am posting it here too for spread:)

This is a case I have been assigned at work and I'd love input from anyone who's tackled something similar.

I'm building a failure prediction model for ~33k chargers. The devices emit data at two very different rates depending on operational state: roughly 1 obs/hour when idle and 1 obs/20s when active with a different feature set in each mode. I want to try predicting failures within a 7 day horizon, but I am open for other suggestions.

The positive rate is around 1% at 30 days and 2% at 90 days with a max of 5% of devices ever failing. Strong per-device behavioral variance makes it hard to even define what "normal" looks like. Devices have different usage patterns and

I'm now thinking about whether the mode shift problem is better solved at the architecture level or the data level. One option I'm considering is two separate RNN encoders for each operational state feeding into a shared decoder. But I'm also open to windowing and sampling approaches. And beyond reweighting and loss skewing what has actually worked for you at sub-2% positive rates in time series?

How would you tackle an issue like this?