A Novel Cross-Domain Data Augmentation and Bearing Fault Diagnosis Method Based on an Enhanced Generative Model

IEEE Transactions on Instrumentation and Measurement, Год журнала: 2024, Номер 73, С. 1 - 9

Опубликована: Янв. 1, 2024

In actual industrial production, differences in production conditions lead to variations the collected data distribution. This gives rise a particular problem: while one set of has complete status available, another only possesses from healthy state. Differences result limitations for diagnosing new condition. To address this challenge, method based on envelope order spectra generation is proposed. Initially, and analysis conducted raw vibration align across different domains extract domain-independent signal components—the spectra. Subsequently, an enhanced Variational Autoencoder Generative Adversarial Network (VAEGAN) trained using The model then employed generate synthetic spectra, serving as augmentation working conditions, thereby achieving cross-domain augmentation. Next, augmented used train generic fault classification, enabling diagnosis. Finally, proposed approach validated by testing it with real Experimental results demonstrate that can reliable fake under diverse accomplishing diagnosis preserving privacy.

Язык: Английский

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Multi-scale adaptive-routing capsule contrastive network-based intelligent fault diagnosis method for rotating machinery under noisy environment and labels

Advanced Engineering Informatics, Год журнала: 2024, Номер 62, С. 102712 - 102712

Опубликована: Июль 20, 2024

Язык: Английский

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KAN-HyperMP: An Enhanced Fault Diagnosis Model for Rolling Bearings in Noisy Environments

Sensors, Год журнала: 2024, Номер 24(19), С. 6448 - 6448

Опубликована: Окт. 5, 2024

Rolling bearings often produce non-stationary signals that are easily obscured by noise, particularly in high-noise environments, making fault detection a challenging task. To address this challenge, novel diagnosis approach based on the Kolmogorov-Arnold Network-based Hypergraph Message Passing (KAN-HyperMP) model is proposed. The KAN-HyperMP composed of three key components: neighbor feature aggregation block, fusion and KANLinear block. Firstly, block leverages hypergraph theory to integrate information from more distant neighbors, aiding reduction noise impact, even when nearby neighbors severely affected. Subsequently, combines features these higher-order with target node's own features, enabling capture complete structure hypergraph. Finally, smoothness properties B-spline functions within Network (KAN) employed extract critical diagnostic noisy signals. proposed trained evaluated Southeast University (SEU) Jiangnan (JNU) Datasets, achieving accuracy rates 99.70% 99.10%, respectively, demonstrating its effectiveness under both noise-free conditions.

Язык: Английский

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An unsupervised domain adaptation method for intelligent fault diagnosis based on target feature enhancement and feature-boundary alignment

Journal of Intelligent Manufacturing, Год журнала: 2025, Номер unknown

Опубликована: Март 9, 2025

Язык: Английский

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Simplicial complexes graph convolution networks with higher-order features learning for limited samples diagnosis

Control Engineering Practice, Год журнала: 2025, Номер 162, С. 106391 - 106391

Опубликована: Май 13, 2025

Язык: Английский

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