Strategies to face imbalanced and unlabelled data in PHM applications.

Accuracy and usefulness of learned data-driven PHM models are closely related to availability and representativeness of data. Notably, two particular problems can be pointed out. First, how to improve the performances of null algorithms in presence of underrepresented data and severe class distribution skews? This is often the case in PHM applications where faulty data can be hard (even dangerous) to gather, and can be sparsely distributed...