Feature Ranking under Industrial Constraints in Continuous Monitoring Applications based on Machine Learning Techniques

The design work-flow of machine learning techniques for continuous monitoring or predictive maintenance in an industrial context is usually a two step procedure: the selection of features to be computed from the observed signals and training of a suitable algorithm with real-life meaningful data, that will be next deployed in the second step. Feature selection is a relevant task since it provides a powerful optimisation of the deployed algorithm performance, for the given training data-set. The paper provides a method for feature ranking and selection that embeds constraints coming from real-life applications, including sensing device specifications, environmental noise, available processing resources, being all these latter aspects not considered in the currently available literature methods for feature selection. A practical case-study in the field on anomaly detection of machines is reported and discussed, in order to show the good properties of the provided method.