MPARN: multi-scale path attention residual network for fault diagnosis of rotating machines

Kim, Hyeongmin; Park, Chan Hee; Suh, Chaehyun; Chae, Minseok; Yoon, Heonjun; Youn, Byeng D.

doi:10.1093/jcde/qwad031

Cited by 10 publications

(2 citation statements)

References 45 publications

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“…Despite significant disparities between source domains, such as differences in class size, fault severity's and location with different load angles, the method shows good adaptation between source and target domains. In overall the suggested technique [9] 08 97.70% KNNITD-TQWT [9] 08 96.90 % MPARN [14] 08 99.49% Proposed Method 08 99.73%…”

Section: Case Western Reserve University Datasetmentioning

confidence: 99%

A hybrid approach for gearbox fault diagnosis based on deep learning techniques

Bessaoudi,

Habbouche,

Benkedjouh

et al. 2024

Int J Adv Manuf Technol

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Section: Case Western Reserve University Datasetmentioning

confidence: 99%

A hybrid approach for gearbox fault diagnosis based on deep learning techniques

Bessaoudi,

Habbouche,

Benkedjouh

et al. 2024

Int J Adv Manuf Technol

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“…This approach aims to improve diagnostic performance in industrial applications. Kim et al [31] proposed a new architecture for multi-scale path-attention residual networks. This method addresses the issue of uncorrelated features at specific time scales that can affect diagnostic performance.…”

Section: Introductionmentioning

confidence: 99%

Multi-scale residual neural network with enhanced gated recurrent unit for fault diagnosis of rolling bearing

Liao,

Fu,

Yang

et al. 2024

Meas. Sci. Technol.

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As the key component of rotating machinery, effective and reliable fault diagnosis of rolling bearing is particularly critical for promoting production safety and economic benefits. The powerful representation learning ability of convolutional neural network (CNN) enables it to effectively extract fault information from vibration signals of rolling bearing. Nevertheless, challenges are faced by CNN in extracting features at multi-scale and capturing temporal features. With regard to this issue, a hybrid deep learning model that incorporates the multi-scale residual neural network (MSRN) with the enhanced gated recurrent unit (EGRU), namely MSRN-EGRU, is proposed in this paper. To begin with, MSRN is designed by introducing multi-scale structure and residual connections into CNN for extracting local features effectively and improving the feature representation of the model. Then, the extracted local features are input into EGRU to further extract temporal features, where EGRU is proposed by improving gated recurrent unit (GRU) structure and embeding scaled exponential liner unit (SELU), which enhances the nonlinear modeling and memory ability. Eventually, the obtained features are processed by α-Dropout and global average pooling (GAP) before being inputted into the softmax layer for fault diagnosis. To validate the effectiveness of the proposed model, three baseline models and two ablation models were employed for comparative experiments with two bearing datasets. The experimental results reveal that the proposed model achieves commendable performance in terms of accuracy, robustness, and convergence for fault diagnosis of rolling bearing.

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