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Journal of Intelligent Manufacturing

Erschienen in:

06.05.2023

A dual-attention feature fusion network for imbalanced fault diagnosis with two-stream hybrid generated data

verfasst von: Chenze Wang, Han Wang, Min Liu

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 4/2024

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Abstract

Deep learning-based fault diagnosis models achieve great success with sufficient balanced data, but the imbalanced dataset in real industrial scenarios will seriously affect the performance of various popular deep learning models. Data generation-based strategy provides a solution by expanding the number of minority samples. However, many data-generation methods cannot generate high-quality samples when the imbalanced ratio is high. To address these problems, a dual-attention feature fusion network (DAFFN) with two-stream hybrid-generated data is proposed. First, the two-stream hybrid generator including a generative model and an oversampling technique is adopted to generate minority fault data. Then, the convolutional neural network is used to extract features from hybrid-generated data. In particular, a feature fusion network with a dual-attention mechanism, i.e., a channel attention mechanism and a layer attention mechanism are designed to learn channel-level and layer-level weights of the features. Extensive results on two bearing datasets indicate that the proposed framework achieves outstanding performance in various high imbalanced-ratio cases.

Vorheriger Artikel A deep learning framework for defect prediction based on thermographic in-situ monitoring in laser powder bed fusion

Nächster Artikel Data-driven indirect punch wear monitoring in sheet-metal stamping processes

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Titel: A dual-attention feature fusion network for imbalanced fault diagnosis with two-stream hybrid generated data
verfasst von: Chenze Wang
Han Wang
Min Liu
Publikationsdatum: 06.05.2023
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 4/2024
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-023-02131-2

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