  For predictive maintenance, we examine one of the largest public datasets for
machine failures derived along with their corresponding precursors as error
rates, historical part replacements, and sensor inputs. To simplify the time
and accuracy comparison between 27 different algorithms, we treat the imbalance
between normal and failing states with nominal under-sampling. We identify 3
promising regression and discriminant algorithms with both higher accuracy
(96%) and twenty-fold faster execution times than previous work. Because
predictive maintenance success hinges on input features prior to prediction, we
provide a methodology to rank-order feature importance and show that for this
dataset, error counts prove more predictive than scheduled maintenance might
imply solely based on more traditional factors such as machine age or last
replacement times.
