I have a component and I need to predict when it will wear out and will need replacement. I monitor, let's say 5 parameters of this component, each one is monitored for every run cycle. So, the dataset can look like this:
No_of_runs Para1 Para2 Para3 Para4 Para5 1 100 32 45 230 86 2 101 34 65 234 90 3 120 24 32 242 80 4 105 45 40 213 75 5 90 42 54 200 77 ... ... ... ... ... ...
In addition to the above, I have a dataset saying at which point this component needed replacement in the past. So, if the above dataset reaches up to 500 rows (run cycles of the component), the other dataset says that the component nedded replacement in cycles 50, 130, 340, 400.
Based on these data, the goal is to create a model able to predict when this component is going to fail in the future giving it the parameters' data up to this time.
I struggle to come up with a solution to the problem and as I have not much of experience in the field, I'm not sure of which approach to take.
It's a supervised problem, but I'm not sure of how to integrate the information of 'degradation' of the component (run cycles) or how to structure my features matrix.