A semi-supervised diagnostic framework based on the surface estimation of faulty distributions

Document Type

Article

Publication Date

3-1-2019

Publication Title

IEEE Transactions on Industrial Informatics

Volume

15

Issue

3

First Page

1277

Keywords

Fault diagnosis, induction motors (IMs), semi-supervised learning (SSL), surface estimation approach

Last Page

1286

Abstract

Design of the data-driven diagnostic systems usually requires to have labeled data during the training session. This paper aims to design a hybrid data-driven framework for diagnosing faults, where the data labels are not available to a large extent. This hybrid framework has five steps for transforming raw vibration signals to informative sets of samples for decision making. It uses several state-of-the-art approaches for feature extraction and semi-supervised feature reduction. The decision-making step uses a number of state-of-the-art semi-supervised learners. This step also comprises a novel surface estimation approach that is developed for SSL. The proposed hybrid framework is applied for diagnosing bearing defects in induction motors and validated based on four scenarios, each of which is experimented with different amounts of labeled samples. The attained diagnostic accuracies show the efficiency of the proposed hybrid framework, including the novel semi-supervised learner in classifying bearing defects, regardless of the number of labeled samples.

DOI

10.1109/TII.2018.2851961

ISSN

15513203

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