  Predicting incoming failures and scheduling maintenance based on sensors
information in industrial machines is increasingly important to avoid downtime
and machine failure. Different machine learning formulations can be used to
solve the predictive maintenance problem. However, many of the approaches
studied in the literature are not directly applicable to real-life scenarios.
Indeed, many of those approaches usually either rely on labelled machine
malfunctions in the case of classification and fault detection, or rely on
finding a monotonic health indicator on which a prediction can be made in the
case of regression and remaining useful life estimation, which is not always
feasible. Moreover, the decision-making part of the problem is not always
studied in conjunction with the prediction phase. This paper aims to design and
compare different formulations for predictive maintenance in a two-level
framework and design metrics that quantify both the failure detection
performance as well as the timing of the maintenance decision. The first level
is responsible for building a health indicator by aggregating features using a
learning algorithm. The second level consists of a decision-making system that
can trigger an alarm based on this health indicator. Three degrees of
refinements are compared in the first level of the framework, from simple
threshold-based univariate predictive technique to supervised learning methods
based on the remaining time before failure. We choose to use the Support Vector
Machine (SVM) and its variations as the common algorithm used in all the
formulations. We apply and compare the different strategies on a real-world
rotating machine case study and observe that while a simple model can already
perform well, more sophisticated refinements enhance the predictions for
well-chosen parameters.
