An Integrated Class-Imbalanced Learning Scheme for Diagnosing Bearing Defects in Induction Motors

Document Type

Article

Publication Date

12-1-2017

Publication Title

IEEE Transactions on Industrial Informatics

Volume

13

Issue

6

First Page

2758

Keywords

Bearing defects, Case Western Reserve University (CWRU), dimensionality reduction (DR), fault diagnosis, feature extraction (FE), feature selection (FS), imbalanced condition, induction motors (IMs)

Last Page

2769

Abstract

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.

DOI

10.1109/TII.2017.2755064

ISSN

15513203

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