Mode-Decoupling Auto-Encoder for Machinery Fault Diagnosis Under Unknown Working Conditions

An, Zenghui; Jiang, Xingxing; Liu, Jie

doi:10.1109/tii.2023.3331129

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…The parameter settings used in this method are the same as those described in this experiment. (4) Comparison method 4: actively imaginative data augmentation [26]. Knowledge learning and sample imagining steps are combined for adversarial training.…”

Section: Gear Variable Speed Data Experimentsmentioning

confidence: 99%

Attention mechanism guided sparse filtering for mechanical intelligent fault diagnosis under variable speed condition

Han,

Wang,

Wan

et al. 2024

Meas. Sci. Technol.

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Variable speed is one of the common working conditions of mechanical equipment,which poses an important challenge to equipment fault diagnosis. The current solutions have the shortcomings of low computational efficiency and large diagnostic errors. The ability of attention mechanism to automatically extract useful features has begun to attract widespread attention in the field of mechanical intelligent fault diagnosis. Combining the advantages of attention mechanism and unsupervised learning, this paper proposes a squeeze-excitation attention guided sparse filtering (SESF) method for mechanical intelligent fault diagnosis method under variable speed. Firstly, the SE attention mechanism is embedded in SF algorithm to guide model training. Then, unsupervised feature extraction is carried out on the variable speed signal samples. The training results are adaptively screened and weighted to make the model pay more attention to the region with the most classify discrimination, so as to improve the feature extraction ability of the model to obtain useful information. Finally, two sets of gear and bearing tests under variable speed condition are adopted to testify the performance of the proposed method. The experimental results show that the SESF method can overcome the influence of variable speed to achieve accurate recognition of different mechanical faults and is superior to the other methods.

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Section: Gear Variable Speed Data Experimentsmentioning

confidence: 99%

Attention mechanism guided sparse filtering for mechanical intelligent fault diagnosis under variable speed condition

Han,

Wang,

Wan

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

Non-stationary mechanical sound source separation: An all-neural beamforming network driven by time–frequency convolution and self-attention

Li,

Ouyang,

et al. 2025

Measurement

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A tied-weight autoencoder for the linear dimensionality reduction of sample data

Kim,

Chu,

Park

et al. 2024

Sci Rep

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Dimensionality reduction is a method used in machine learning and data science to reduce the dimensions in a dataset. While linear methods are generally less effective at dimensionality reduction than nonlinear methods, they can provide a linear relationship between the original data and the dimensionality-reduced representation, leading to better interpretability. In this research, we present a tied-weight autoencoder as a dimensionality reduction model with the merit of both linear and nonlinear methods. Although the tied-weight autoencoder is a nonlinear dimensionality reduction model, we approximate it to function as a linear model. This is achieved by removing the hidden layer units that are largely inactivated by the input data, while preserving the model’s effectiveness. We evaluate the proposed model by comparing its performance with other linear and nonlinear models using benchmark datasets. Our results show that the proposed model performs comparably to the nonlinear model of a similar autoencoder structure to the proposed model. More importantly, we show that the proposed model outperforms the linear models in various metrics, including the mean square error, data reconstruction, and the classification of low-dimensional projections of the input data. Thus, our study provides general recommendations for best practices in dimensionality reduction.

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Mode-Decoupling Auto-Encoder for Machinery Fault Diagnosis Under Unknown Working Conditions

Cited by 3 publications

References 38 publications

Attention mechanism guided sparse filtering for mechanical intelligent fault diagnosis under variable speed condition

Attention mechanism guided sparse filtering for mechanical intelligent fault diagnosis under variable speed condition

Non-stationary mechanical sound source separation: An all-neural beamforming network driven by time–frequency convolution and self-attention

A tied-weight autoencoder for the linear dimensionality reduction of sample data

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