Information Fusion and Semi-Supervised Deep Learning Scheme for Diagnosing Gear Faults in Induction Machine Systems

There has been an increasing interest in the design of intelligent diagnostic systems for industrial applications. The key requirement in the design of practical diagnostic systems is the ability for decision making in high-dimensional feature spaces, where the prior knowledge about the system state...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2019-08, Vol.66 (8), p.6331-6342
Main Authors: Razavi-Far, Roozbeh, Hallaji, Ehsan, Farajzadeh-Zanjani, Maryam, Saif, Mehrdad, Kia, Shahin Hedayati, Henao, Humberto, Capolino, Gerard-Andre
Format: Article
Language:English
Subjects:
Data integration
Decision making
Deep learning
Defects
Design engineering
Diagnostic software
Diagnostic systems
Electromechanical systems
Engineering Sciences
Fault diagnosis
Feature extraction
Gearboxes
Gears
induction machines
Induction motors
Industrial applications
information fusion
Machine learning
multisensor
Multisensor fusion
semi-supervised learning (SSL)
simultaneous defects
Training
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Summary:There has been an increasing interest in the design of intelligent diagnostic systems for industrial applications. The key requirement in the design of practical diagnostic systems is the ability for decision making in high-dimensional feature spaces, where the prior knowledge about the system states in terms of labels is very limited. Moreover, the problem of diagnosing simultaneous defects is rarely addressed on real industrial applications. This paper aims to develop a semi-supervised deep-learning scheme for diagnosing multiple defects including simultaneous ones in a gearbox directly connected to an induction machine shaft. This scheme consists of two main modules: information fusion and decision making. The former integrates captured multiple sensory streams into a very high dimensional feature space. The latter uses a semi-supervised deep learning procedure to minimize the human interaction during the training and maximize the diagnostic efficiency. This scheme facilitates learning and diagnosing defects under harshest conditions 1) where only a few number of labeled samples are collected together with a large number of unlabeled samples, and 2) in a very high-dimensional feature space. Several state-of-the-art semi-supervised and supervised learners have also been included in the scheme, enabling a comparative experiment for diagnosing simultaneous defects.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2018.2873546