Advanced Functional Materials, EarlyView.A transparent, laser‐microscribed glass platform enables cancer diagnosis within 1 h—much faster than histology, which takes days, and free from the chemical or contrast risks of MRI or CT scans. The antibody‐functionalized rough glass surface captures viable cancer cells directly from suspension, allowing instant optical readout and offering a rapid ...
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Advanced fault diagnosis in milling cutting tools using vision transformers with semi-supervised learning and uncertainty quantification. [PDF]
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Research on bearing fault diagnosis based on machine learning and SHAP interpretability analysis. [PDF]
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SCBM-Net: a multimodal feature fusion-based dual-channel method for bearing fault diagnosis. [PDF]
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Fault Types and Diagnostic Methods of Manipulator Robots: A Review. [PDF]
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Improved Variational Mode Decomposition Based on Scale Space Representation for Fault Diagnosis of Rolling Bearings. [PDF]
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Adaptive blind deconvolution decomposition and its application in composite fault diagnosis of rolling bearings. [PDF]
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Zero-shot learning compound fault diagnosis of bearings
2021 International Joint Conference on Neural Networks (IJCNN), 2021The compound fault signal of bearings is coupled and complex, thereby compound fault diagnosis is a difficult problem in bearing fault diagnosis. The existing deep learning models can extract fault features when there are a large number of labeled compound fault samples.
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Zero-shot learning for compound fault diagnosis of bearings
Expert Systems with Applications, 2022Abstract Due to the concurrency and coupling of various types of faults, and the number of possible fault modes grows exponentially, thereby compound fault diagnosis is a difficult problem in bearing fault diagnosis. The existing deep learning models can extract fault features when there are a large number of labeled compound fault samples.
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A compound fault diagnosis model for gearboxes using correlation information between single faults
Measurement Science and Technology, 2023Abstract Gearboxes are key components of rotating machinery. Performing intelligent fault diagnosis of gearboxes with condition-based monitoring information helps to make reliable decisions on equipment operation and maintenance. Besides single faults, compound faults also are common failure forms of gearboxes.
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