A Current Signal-Based Adaptive Semisupervised Framework for Bearing Faults Diagnosis in Drivetrains

In most practical applications of fault diagnosis methods, two problems will inevitably arise. First, limited by the monitored object itself and its environment, accelerators are difficult to install. Second, industrial applications lack data with fault labels, which limits the use of data-driven-ba...

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Bibliographic Details
Published in:	IEEE transactions on instrumentation and measurement Vol. 70; pp. 1 - 12
Main Authors:	Li, Jie, Wang, Yu, Zi, Yanyang, Sun, Xiaojie, Yang, Ying
Format:	Journal Article
Language:	English
Published:	New York IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accelerators Bearing faults current signals Environmental monitoring Fault detection Fault diagnosis Feature extraction Feature recognition Gallium nitride generative adversarial network Generative adversarial networks Generators Industrial applications Normal distribution Powertrain Self organizing maps semisupervised learning signal processing Torque Training
ISSN:	0018-9456, 1557-9662
Online Access:	Get full text
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