An Integrated Multitasking Intelligent Bearing Fault Diagnosis Scheme Based on Representation Learning Under Imbalanced Sample Condition

Accurate bearing fault diagnosis is of great significance of the safety and reliability of rotary mechanical system. In practice, the sample proportion between faulty data and healthy data in rotating mechanical system is imbalanced. Furthermore, there are commonalities between the bearing fault det...

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Vydáno v:IEEE transaction on neural networks and learning systems Ročník 35; číslo 5; s. 6231 - 6242
Hlavní autoři: Zhang, Jiusi, Zhang, Ke, An, Yiyao, Luo, Hao, Yin, Shen
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.05.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Classification
Datasets
Fault classification
Fault detection
Fault diagnosis
Feature extraction
imbalanced sample condition
Machine learning
Mechanical systems
modified denoising autoencoder with self-attention mechanism for bottleneck layer (MDAE-SAMB)
Multitasking
Neurons
Representation learning
Representations
Rotor dynamics
Task analysis
Training
Transfer learning
unknown fault identification
ISSN:2162-237X, 2162-2388, 2162-2388
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Popis
Shrnutí:Accurate bearing fault diagnosis is of great significance of the safety and reliability of rotary mechanical system. In practice, the sample proportion between faulty data and healthy data in rotating mechanical system is imbalanced. Furthermore, there are commonalities between the bearing fault detection, classification, and identification tasks. Based on these observations, this article proposes a novel integrated multitasking intelligent bearing fault diagnosis scheme with the aid of representation learning under imbalanced sample condition, which realizes bearing fault detection, classification, and unknown fault identification. Specifically, in the unsupervised condition, a bearing fault detection approach based on modified denoising autoencoder (DAE) with self-attention mechanism for bottleneck layer (MDAE-SAMB) is proposed in the integrated scheme, which only uses the healthy data for training. The self-attention mechanism is introduced into the neurons in the bottleneck layer, which can assign different weights to the neurons in the bottleneck layer. Moreover, the transfer learning based on representation learning is proposed for few-shot fault classification. Only a few fault samples are used for offline training, and high-accuracy online bearing fault classification is achieved. Finally, according to the known fault data, the unknown bearing faults can be effectively identified. A bearing dataset generated by rotor dynamics experiment rig (RDER) and a public bearing dataset demonstrates the applicability of the proposed integrated fault diagnosis scheme.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2022.3232147