Journal
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 88, Issue -, Pages 413-427Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2016.11.004
Keywords
Evolving environment; Feature selection; Concept drift; Drift detection; Fault diagnostics; Bearing faults
Categories
Funding
- China Scholarship Council
- Politecnico di Milano [201206110018]
- China NSFC [71231001]
- European Union Project INNovation through Human Factors in risk analysis and management (INNHF) - 7th framework program FP7-PEOPLE- Initial Training Network: Marie-Curie Action
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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.
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