Assessment of multi-dimensional joint probability distribution for uncertain mechanical strength parameters under small sample test data

Cao, Jiazeng; Wang, Tao; Sheng, Mao; Huang, Yingying; Mo, Pinqiang; Zhou, Guoqing

doi:10.1016/j.probengmech.2023.103511

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article4

Other1

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 5 publications

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Reliability analysis of cutting slopes under rainfall conditions considering copula dependence between shear strengths

Liu,

Xu,

Zhu

et al. 2024

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

Reliability analysis of cutting slopes under rainfall conditions considering copula dependence between shear strengths

Liu,

Xu,

Zhu

et al. 2024

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

Statistical model calibration of correlated unknown model variables through identifiability improvement

Choo,

Jung,

et al. 2024

Probabilistic Engineering Mechanics

View full text Add to dashboard Cite

Deep network fault diagnosis for imbalanced small-sized samples via a coupled adversarial autoencoder based on the Bayesian method

Zhang,

Wang,

Zhou

et al. 2024

Review of Scientific Instruments

View full text Add to dashboard Cite

Deep network fault diagnosis methods heavily rely on abundant labeled data for effective model training. However, small-sized samples and imbalanced samples often lead to insufficient features, resulting in accuracy degradation and even instability in the diagnosis model. To address this challenge, this paper introduces a coupled adversarial autoencoder (CoAAE) based on the Bayesian method. This model aims to solve the issue of insufficient samples by generating fake samples and integrating them with the original ones. Within the CoAAE framework, the probability density distribution of the original data is captured using an encoder and fake samples are generated by random sampling from this distribution and decoding them. This process is the adversarial interaction between the encoder and a classifier to obtain the prior distribution of the encoder’s parameters. The encoder’s parameters are updated through the decoder’s reconstruction process, leading to the posterior distribution. Concurrently, the decoder is trained to enhance its ability to reconstruct samples accurately. To address the imbalance in the original samples, a parallel coupled network is employed. This network shares the weights of the extraction layer in the encoder, enabling it to learn the joint distribution between fault-related and normal samples. To evaluate the effectiveness of the proposed data augmentation method, experiments were conducted on a bearing database from Case Western Reserve University using ResNet18 as the deep learning diagnosis model representative. The results demonstrate that CoAAE can effectively augment imbalanced datasets and outperform other advanced methods.

show abstract

Assessment of multi-dimensional joint probability distribution for uncertain mechanical strength parameters under small sample test data

Cited by 5 publications

References 33 publications

Reliability analysis of cutting slopes under rainfall conditions considering copula dependence between shear strengths

Reliability analysis of cutting slopes under rainfall conditions considering copula dependence between shear strengths

Statistical model calibration of correlated unknown model variables through identifiability improvement

Deep network fault diagnosis for imbalanced small-sized samples via a coupled adversarial autoencoder based on the Bayesian method

Contact Info

Product

Resources

About