A Semi-Supervised Feature Selection Approach for Fault Diagnostics in Evolving Environments



Published Jul 5, 2016
Yang Hu Piero Baraldi Francesco Di Maio Enrico Zio


This paper introduces a Semi-Supervised Feature Selection (SSFS) approach for selecting the most suitable features for fault diagnostics in evolving environments. The effectiveness of the proposed SSFS approach is verified with respect to an application concerning the classification of the defect type of bearings in Fully Electric Vehicles operating at different loads. The results show that SSFS allows adapting the diagnostic model to the varying load by updating the set of features used for the classification and achieves more satisfactory diagnostic accuracy than the traditional diagnostic models. The proposed diagnostic approach can contribute significantly to the maintenance practice of components such as gearboxes, alternators, shafts and pumps, whose working conditions are usually characterized by evolving environment.

How to Cite

Hu, Y., Baraldi, P., Maio, F. D., & Zio, E. (2016). A Semi-Supervised Feature Selection Approach for Fault Diagnostics in Evolving Environments. PHM Society European Conference, 3(1). https://doi.org/10.36001/phme.2016.v3i1.1627
Abstract 68 | PDF Downloads 55



feature selection, fault diagnostics, evolving environment

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