The big picture of the problem is the following : predicting the failure on engine by predicting temperature of the engine because that is intuitively the main reason of failure. The first thing I want to do is to check which other variables influence the temperature, like torque of the engine, functioning mode of the engine etc. Why? Because those are variables we can change in real life and thus avoid high temperatures, and thus the failure.
So, my question is how to find on which variables the temperature is dependent and how much. As the temperature is dependent on the time we're in the case of a time series problem but not all the variables are dependent on their past values. Therefore, I am not sure that auto-regressive models could work.
First thing that came in mind was to check whether there is a linear relationship. But by reflecting on how physically a temperature will evolve I'm pretty sure it is exponentially, so perhaps just taking the natural logarithm we can transform it into a linear problem and then apply a linear regression. The problem is that won't capture the time dependency of the temperatures. I looked into autoregressive models but I'm not sure they will work. All I want is to see which variables have an impact on temperature not to predict the temperature for now.