Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 15, Issue 3, Pages 1277-1286Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2018.2851961
Keywords
Fault diagnosis; induction motors (IMs); semi-supervised learning (SSL); surface estimation approach
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Funding
- Natural Sciences and Engineering Research Council of Canada [401226689, TII-17-2918]
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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.
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