期刊
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
卷 88, 期 -, 页码 413-427出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2016.11.004
关键词
Evolving environment; Feature selection; Concept drift; Drift detection; Fault diagnostics; Bearing faults
资金
- 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
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据