Related references
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Article
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Taotao Zhou et al.
Summary: This paper proposes a reliable fault diagnosis method based on deep learning, which improves the reliability and accuracy of deep learning-based fault diagnosis in safety-critical applications by quantifying predictive uncertainty and introducing a predictive risk-aware strategy.
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Summary: A comprehensive dynamic model of ball bearings with flexible structures is developed in this study. The flexibility of the cage is represented by splitting it into discrete segments linked by nonlinear springs. The effect of cage flexibility on cage clearance is considered, as well as the race flexibility generated by the assembly status variation. The results show that rational design for structure flexibility can optimize bearing rotary performance.
TRIBOLOGY INTERNATIONAL
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Summary: The success of deep learning and transfer learning has expanded the scope of fault diagnosis, especially in improving diagnosis accuracy under multiple working conditions. However, most existing approaches do not account for the diversity of fault mode distributions and weaken the generalization to imbalanced domain adaptation scenarios. This work proposes a novel deep imbalanced domain adaptation framework for fault diagnosis, which overcomes class-imbalanced label shift and improves cross-domain generalization for IDA tasks.
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Summary: This paper proposes a novel fault diagnosis approach for bevel gearboxes based on semi-supervised probability support matrix machine (SPSMM) and infrared imaging. It directly classifies 2D matrix data, uses a probability output strategy to estimate posterior class probabilities, and employs a semi-supervised learning framework to alleviate the problem of insufficient labeled samples.
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Da-wei Gao et al.
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MECHANICAL SYSTEMS AND SIGNAL PROCESSING
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Shen Liu et al.
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MECHANICAL SYSTEMS AND SIGNAL PROCESSING
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Xiaohu Li et al.
Summary: Methods with multi-sensor data fusion improve the accuracy and robustness of bearing fault diagnosis. This paper proposes a novel model of multi-layer deep fusion network with attention mechanism (AMMFN) to enhance the information interaction and achieve adaptive hierarchical fusion. Extensive experiments demonstrate its higher accuracy and stronger generalization ability compared to other methods.
Article
Computer Science, Artificial Intelligence
Wei Li et al.
Summary: This study proposes a modified auxiliary classifier GAN (MACGAN) model to address the issue of insufficient samples in fault diagnosis. By improving the ACGAN framework and introducing techniques such as Wasserstein distance and spectral normalization, the proposed method can generate high-quality multi-mode fault samples more effectively, improving the accuracy and stability of fault diagnosis models.
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Ziling Huang et al.
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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
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Liang Chen et al.
Summary: In this article, a generic domain-regressive framework called ADIG is proposed for fault diagnosis, which leverages adversarial learning to extract domain-invariant knowledge and generalize knowledge from the source domain to diagnose unseen but related target domain signals. Customized strategies of feature normalization and adaptive weight are proposed to improve diagnosis performance.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
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Jimeng Li et al.
Summary: This paper proposes a sparse representation method based on a period-assisted adaptive parameterized wavelet dictionary to accurately extract periodic transient features of rolling bearing faults from noise interference containing harmonics and large-amplitude random impulses. Experimental results demonstrate that the proposed method can more accurately extract periodic transient features in vibration signals, providing an effective analysis tool for rolling bearing fault detection.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
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Tianfu Li et al.
Summary: Deep learning methods have advanced the field of Prognostics and Health Management, but handling irregular data in non-Euclidean space remains a challenge. Research has proposed a practical guideline for utilizing graph neural networks for intelligent fault diagnostics and prognostics, and established a framework based on GNN for this purpose.
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Chen Yin et al.
Summary: This paper proposes a method based on improved ensemble noise-reconstructed empirical mode decomposition (IENEMD) and adaptive threshold denoising (ATD) for the weak fault feature extraction of rolling bearings. Experimental results demonstrate that the proposed method outperforms other techniques in extracting weak fault features of rolling bearings.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
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Huaitao Shi et al.
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MECHANICAL SYSTEMS AND SIGNAL PROCESSING
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He Li et al.
Summary: A model is proposed to assess the failure rates of components of floating offshore wind turbines based on the knowledge of failure data of corresponding structures of onshore wind turbines with sufficient failure data. The results indicate that the failure rates of components of floating offshore wind turbines are higher than those of onshore devices. The model presented contributes to the risk, failure, and reliability analysis and assessment under insufficient data conditions.
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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
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Jingjie Luo et al.
Summary: In this paper, a modified deep subdomain adaptation network (MDSAN) is proposed for handling speed fluctuation in practical and challenging cross-domain diagnostic scenarios. The method utilizes a shared feature extraction module guided by a multi-headed self-attention mechanism to extract representative features, and a new trade-off factor is designed to improve convergence performance and optimization process.
JOURNAL OF MANUFACTURING SYSTEMS
(2022)
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Shaoke Wan et al.
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Yulin Ma et al.
Summary: This paper introduces an unsupervised domain adaptation strategy for intelligent fault diagnosis, proposing the CKADA method for fault knowledge transfer. The method effectively addresses the issues of label noise and domain representation through the design of a convolutional kernel aggregated layer, a classification bridge layer, and a discrimination bridge layer.
RELIABILITY ENGINEERING & SYSTEM SAFETY
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Chan Hee Park et al.
Summary: This study proposes a novel method for fault diagnosis using deep learning and convolutional neural networks. By transforming fault-related signatures in motor stator current signals into a two-dimensional input image, the proposed method achieves accurate fault diagnosis of permanent magnet synchronous motors. The effectiveness of the proposed method is experimentally validated using a surface mounted PMSM under various operating conditions.
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Summary: In this paper, a global contextual residual convolutional neural network is proposed for motor fault diagnosis in variable-speed scenarios. The network adopts a hierarchical structure to utilize features from all intermediate layers and explores multiscale information. It also introduces a global context module and a multi-feature fusion layer to improve the diagnostic performance.
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Minqiang Deng et al.
Summary: This paper presents a fault diagnosis method based on Order Spectrum Transfer, which establishes intelligent fault diagnosis models for rotating components by utilizing monitoring data from other related machines. Experimental results demonstrate the effectiveness and superiority of the proposed method in real applications lacking complete samples.
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Computer Science, Artificial Intelligence
Dawei Gao et al.
Summary: Traditional diagnostic methods are less accurate for bearings under strong noise conditions, making the extraction of weak fault features a research focus. This paper proposes a novel method consisting of multi-channel continuous wavelet transform and convolution-feature-based recurrent neural network, which effectively improves the diagnostic efficiency for bearings.
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Min Xia et al.
Summary: The intelligent fault diagnosis framework based on DT and deep transfer learning enables accurate machine fault diagnosis with limited measured data. This method outperforms other state-of-the-art data-driven methods in achieving intelligent fault diagnosis.
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(2021)
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Xin Li et al.
Summary: The proposed fault diagnosis method combining feature-fusion covariance matrix and multi-Riemannian kernel ridge regression can effectively improve the fault diagnosis performance of gearboxes. Experimental results demonstrate that this approach consistently exhibits excellent diagnostic performance on multi-sensor datasets.
RELIABILITY ENGINEERING & SYSTEM SAFETY
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Bing Wu et al.
Summary: This study addresses the safety management of electric vehicles during transportation on RoPax ships, developing a Bayesian Network model to analyze influencing factors and propose countermeasures. The findings suggest that it is better not to charge electric cars during transportation on RoPax ships, as this increases the probability of explosions.
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