I have a machine that moves with one axis in the same direction (basic position A to end position B). While driving, the torque is measured and recorded every 10 milliseconds. This looks something like this:
Time [t] | Torque [Nm]
0 | 2.5
10 | 4.0
20 | 4.7
30 | 5.6
40 | 6.0
50 | 5.5
60 | 5.1
70 | 3.2
80 | 2.1
90 | 1.5
100 | 0.2
The torque is therefore almost identical for a new machine. On a "very old" machine, the situation is different: the torque is constantly increased (signs of axis wear), or there are some outliers in the record (e.g., torque greatly increased at 50 milliseconds compared to a new machine).
What I need now is a prediction for wear or failure of the axis!
What I have:
Very much data from an axis movement coming from a new machine (the data should therefore be considered "GOOD").
What I do not have:
Data of an axis movement of an old machine (defective axis, increased torque, axis wear, etc.)
My basic idea is:
I want to use Machine Learning to train the data to the "GOOD" state. With the help of machine learning, a prediction (probability of a defect or wear) or a classification (GOOD or BAD) should then be carried out.
My approach:
Regression or classification (supervised learning)
My question is:
Does this approach make sense, or would you choose a completely different approach? Is it even possible what I intend to do?
Thanks in advance