Identify "steady-state" time series window
This could be defined as a classification problem over your time series.
One approach I can think of is to build an LSTM based classifier with a couple of dense layers for binary classification (whether the window is steady or not) and define your own loss function.
So in theory, you're training the LSTM network to sample batches of your time series and classify them as steady state or not.