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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Veröffentlicht in:IEEE transactions on instrumentation and measurement Jg. 70; S. 1 - 12
Hauptverfasser: Li, Jie, Wang, Yu, Zi, Yanyang, Sun, Xiaojie, Yang, Ying
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
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Zusammenfassung: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-based methods. To solve these problems, a current signal-based adaptive semisupervised framework (C-ASSF) is proposed. In C-ASSF, the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is adopted to extract recognizable features from only normal current signals. Subsequently, since WGAN-GP pays too much attention to body signals and ignores the changes caused by faults, the line spectrum feature extraction (LSFE) technique is utilized to remove the main frequency component of the current signal specifically. Finally, an index indicating the degree of deviation from the normal distribution is introduced to identify external bearing faults in drivetrains. Two groups of different experimental data sets are applied to verify the performance of C-ASSF. The results show that C-ASSF is superior to existing methods, such as self-organizing map (SOM) and stack autoencoder (SAE), and can not only identify faults in drivetrains but also identify different fault classes.
Bibliographie:ObjectType-Article-1
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content type line 14
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2020.3046051