  The goal of predictive maintenance is to forecast the occurrence of faults of
an appliance, in order to proactively take the necessary actions to ensure its
availability. In many application scenarios, predictive maintenance is applied
to a set of homogeneous appliances. In this paper, we firstly review taxonomies
and main methodologies currently used for condition-based maintenance;
secondly, we argue that the mutual dissimilarities of the behaviours of all
appliances of this set (the "cohort") can be exploited to detect upcoming
faults. Specifically, inspired by dissimilarity-based representations, we
propose a novel machine learning approach based on the analysis of concurrent
mutual differences of the measurements coming from the cohort. We evaluate our
method over one year of historical data from a cohort of 17 HVAC (Heating,
Ventilation and Air Conditioning) systems installed in an Italian hospital. We
show that certain kinds of faults can be foreseen with an accuracy, measured in
terms of area under the ROC curve, as high as 0.96.
