Classification and calibration techniques in predictive maintenance: A comparison between GMM and a custom one-class classifier

Modeling and predicting failures in the field of predictive maintenance is a challenging task. An important issue of an intelligent predictive maintenance system, exploited also for Condition Based Maintenance applications, is the failure probability estimation that can be found uncalibrated for most standard and custom classifiers grounded on Machine learning. In this paper are compared two classification techniques on a data set of faults collected in the real-world power grid that feeds the city of Rome, one based on a hybrid evolutionary-clustering technique, the other based on the well-known Gaussian Mixture Models setting. While the former adopts directly a custom-based weighted dissimilarity measure for facing unstructured and heterogeneous data, the latter needs a specific embedding technique step performed before the training procedure. Results show that both approaches reach good results with a different way of synthesizing a model of faults and with different structural complexities. Furthermore, besides the classification results, it is offered a comparison of the calibration status of the estimated probabilities of both classifiers, which can be a bottleneck for further applications and needs to be measured carefully