A Systematic Semi-Supervised Self-adaptable Fault Diagnostics approach in an evolving environment

Yang Hu, Piero Baraldi, Francesco Maio and Enrico Zio ()
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Yang Hu: Dipartimento di Energia [Milano] - POLIMI - Politecnico di Milano [Milan]
Piero Baraldi: Dipartimento di Energia [Milano] - POLIMI - Politecnico di Milano [Milan]
Francesco Maio: Dipartimento di Energia [Milano] - POLIMI - Politecnico di Milano [Milan]
Enrico Zio: SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec - Ecole Centrale Paris - Ecole Supérieure d'Electricité - SUPELEC (FRANCE) - CentraleSupélec - EDF R&D - EDF R&D - EDF - EDF, LGI - Laboratoire Génie Industriel - EA 2606 - CentraleSupélec

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Abstract: Fault diagnostic methods are challenged by their applications to industrial components operating in evolving environments of their working conditions. To overcome this problem, we propose a Systematic Semi-Supervised Self-adaptable Fault Diagnostics approach (4SFD), which allows dynamically selecting the features to be used for performing the diagnosis, detecting the necessity of updating the diagnostic model and automatically updating it. Within the proposed approach, the main novelty is the semi-supervised feature selection method developed to dynamically select the set of features in response to the evolving environment. An artificial Gaussian and a real world bearing dataset are considered for the verification of the proposed approach.

Keywords: Evolving environment; Feature selection; Concept drift; Drift detection; Fault diagnostics; Bearing faults (search for similar items in EconPapers)
Date: 2017-05
Note: View the original document on HAL open archive server: https://hal.science/hal-01652242
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Published in Mechanical Systems and Signal Processing, 2017, 88, pp.413 - 427. ⟨10.1016/j.ymssp.2016.11.004⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01652242

DOI: 10.1016/j.ymssp.2016.11.004

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