期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 13, 期 6, 页码 2758-2769出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2017.2755064
关键词
Bearing defects; Case Western Reserve University (CWRU); dimensionality reduction (DR); fault diagnosis; feature extraction (FE); feature selection (FS); imbalanced condition; induction motors (IMs)
类别
资金
- Natural Sciences and Engineering Research Council of Canada
This paper focuses on the development of an integrated scheme for diagnosing bearing defects in induction motors, under the class-imbalanced condition. This scheme comprises of four main modules: segmentation, feature extraction, feature reduction, and fault classification. Various state-of-the-art techniques have been devised in the feature extraction and reduction modules to extract informative sets of features from a raw vibration signal, filter redundant features, and produce the most distinct features for the following module. The fault classification module adapts various state-of-the-art class-imbalanced learning techniques for diagnosing bearing defects. This module contains a novel imputation-based oversampling technique for class-imbalanced learning. This integrated diagnostic scheme is evaluated on three experimental scenarios with different imbalance ratios. The reasonable diagnostic performances confirm the ability of the proposed novel class-imbalanced learning technique in diagnosing bearing defects, independently from the imbalance ratios.
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