Detecting events in time series data
Your first step is to characterize what effect you expect an event to have on your signals.  Does it change the mean?  Increase the mean?  Change the variability?  The more you can say about the type of effect it will have, the more specific a test you'll be able to build, and thus the more effective any analysis is likely to be.

Then, your second step is to apply appropriate techniques from the statistics literature on time series analysis.  There are many techniques that look applicable here:


You could look for change points, where the mean value changes.  For instance, the CUSUM procedure might be useful.  Look into the theory on change point detection; there's lots of work on it.  See also https://stats.stackexchange.com/questions/tagged/change-point.
You might also find literature that describes this as looking for a level shift in time series data.  It's basically the same thing.
You might be looking for a "structural change" in the signal, which is a bit more broad/general than change point analysis.  See, e.g., https://stats.stackexchange.com/questions/tagged/structural-change.


I recommend you start with some exploratory analysis.  Based on the underlying physics/mechanics of how your system works, try to identify some ways that an event might affect the resulting signals.  Also, do some exploratory data analysis: find some examples of known events, visualize the signals during those time periods, and hypothesize some feature values that seem to be affected during the signal (e.g., the mean got larger; the standard deviation got larger; the mean of the absolute value of the first derivative got larger; something like that).  Finally, look for appropriate techniques in the statistical literature.  If you can characterize the effect of an event well, you might be able to use change point detection techniques that will work well.  If you can't characterize the effect of an event, you might need to fall back to some kind of machine learning, trained using a large training set... but you should still do each of the steps I listed, to help you identify what features might be useful to use in your machine learning model.