[D] Time series analysis for machine employment support
I have a (physical) machine that can be tuned by adjusting the values of some parameters A_1, …, A_n (n is around 10). This tuning affects some secondary parameters B_1, …, B_m that cannot be tuned by hand. The machine continuously produces an output X, and by looking at X in a time window, it is possible to decide if the machine was running stable or unstable.
All this information was logged for the past ~10 years, that is roughly around 25M data points.
The tuning of the machine is really complicated, as it can react very sensibly to parameter adjustments and also their influence is not quite well understood, so specialist interventions are needed to keep the machine running at a reasonable performance. The goal is to train a ML model that can support these interventions and generate some insights into how the parameters are related to the stability. For example we would be interested in something like ‘If you raise A_1, you need to lower A_2 in order for the machine to remain stable’ or ‘raising A_1 will increase B_1 in a few hours’.
Up until now we ignored the time component and only ran some clustering to find out which settings were used when the machine was running stable and which were used when it was running unstable. Sadly, the used settings were are greatly (it could have been stable with A_1=100 and A_1=300) and a usually a single setting could lead to a stable as well as an unstable machine, so the time information is crucial.
I am looking for ideas how to approach this task. I was thinking about sub dimensional motif discovery to find typical patterns, but I’m unsure how to link these patterns together.