Generic meta-transfer learning model with special neuronal processing parameters for few-shot fault bearing diagnosis

Peiqi WANG; Changqing SHEN; Bojian CHEN; Juanjuan SHI; Weiguo HUANG; Zhongkui ZHU

doi:10.51393/j.jamst.2023007

Volume 3 Issue 3

Jun. 2023

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Peiqi WANG, Changqing SHEN, Bojian CHEN, Juanjuan SHI, Weiguo HUANG, Zhongkui ZHU. Generic meta-transfer learning model with special neuronal processing parameters for few-shot fault bearing diagnosis[J]. Journal of Advanced Manufacturing Science and Technology , 2023, 3(3): 2023007. doi: 10.51393/j.jamst.2023007

Citation:

Peiqi WANG, Changqing SHEN, Bojian CHEN, Juanjuan SHI, Weiguo HUANG, Zhongkui ZHU. Generic meta-transfer learning model with special neuronal processing parameters for few-shot fault bearing diagnosis[J]. Journal of Advanced Manufacturing Science and Technology , 2023, 3(3): 2023007. doi: 10.51393/j.jamst.2023007

Citation:

Peiqi WANG, Changqing SHEN, Bojian CHEN, Juanjuan SHI, Weiguo HUANG, Zhongkui ZHU. Generic meta-transfer learning model with special neuronal processing parameters for few-shot fault bearing diagnosis[J]. Journal of Advanced Manufacturing Science and Technology , 2023, 3(3): 2023007. doi: 10.51393/j.jamst.2023007

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Generic meta-transfer learning model with special neuronal processing parameters for few-shot fault bearing diagnosis

doi: 10.51393/j.jamst.2023007

a School of Rail Transportation, Soochow University, Suzhou 215131, China;
b Intelligent Urban Rail Engineering Research Center of Jiangsu Province, Suzhou 215131, China

Funds:

This work is financially supported by the National Natural Science Foundation of China (No. 52272440), and Soochow Science and Technology Innovation Talents Program (Nos. WC202168 and ZXL2022027).

Received Date: 2023-04-05
Rev Recd Date: 2023-04-27

Available Online: 2023-06-02

Publish Date: 2023-05-19

Abstract

Abstract

The society is now in the data-rich environment, and deep learning is widely used in bearing fault diagnostic technology due to the advancement of information technology. These methods typically need a large amount of data to support. However, in some practical cases, only few of samples are frequently available when a fault occurs rather than adequate data to be analyzed. This situation indicates that bearing fault diagnostic problems are frequently fewshot problems. In this work, a generic meta-transfer learning model with special neuronal processing parameters (MSNPP) is proposed. MSNPP avoids the issue of overfitting commonly encountered in traditional meta-learning approaches when solving few-shot problems and maintains excellent performance when extracting features with deep networks. Moreover, MSNPP discovers the connection between different tasks by analyzing a few samples and quickly adapts to new tasks. In MSNPP, a technique known as neuron transfer (NT) is used to manipulate neurons by scaling and shifting them. The scaling and shifting parameters are used as meta-learning hyperparameters to transfer within different tasks, which is the work of NT. Experimental result shows that MSNPP prevents the issue of overfitting in conventional meta-learning approaches and achieves satisfactory accuracy and robustness when solving few-shot problems in fault diagnosis.
- Meta-learning,
- Fault diagnosis,
- Rotating machinery,
- Few-shot learning,
- Overfitting

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References(23)

References

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