I have a dataset of 7 trips on a vehicle, where a component fault occurred on the 4th trip and was fixed following that. The goal is to predict when the fault will occur for similar datasets. The dataset contains 9 parameters of numerical sensor data for each trip.
I was requested to generate statistical features for each parameter, plot them, and pick the best ones which show an observable "pattern" or some form of differentiation between the pre-fault trips, the trip where the fault occurred, and the trips following the fault repair.
Is this method a valid form of feature selection? Isn't one of the benefits of feature selection to pick out features that might not have observable patterns, but still have predictive power?