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.

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

HU, YANG;BARALDI, PIERO;DI MAIO, FRANCESCO;ZIO, ENRICO
2017

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.
Bearing faults; Concept drift; Drift detection; Evolving environment; Fault diagnostics; Feature selection; Control and Systems Engineering; Signal Processing; Civil and Structural Engineering; Aerospace Engineering; Mechanical Engineering; Computer Science Applications1707 Computer Vision and Pattern Recognition
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1022915
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