A dissimilarity-based approach to predictive maintenance with application to HVAC systems
This work addresses predictive maintenance for HVAC systems in hospitals, offering a specific improvement but is incremental in nature.
The paper tackled predictive maintenance for HVAC systems by proposing a novel machine learning approach that uses mutual dissimilarities in appliance behaviors to detect faults, achieving an accuracy of up to 0.96 AUC on a dataset of 17 systems.
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.