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Yue Yu

Politecnico di Milano

Italy

Research Interests

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Professors in Italy

Articles (14)

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2026

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2026

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2026

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2026

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2026

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2026

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2026

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2025

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2025

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2024

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2024

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2024

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2024

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2023

Collaborators (6)

Ahmet Enis Cetin

Professor

University of Illinois Chicago (UIC)

UNITED STATES

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Marek Pawelczyk

Silesian University of Technology

POLAND

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PRADEEP KUNDU

Assistant Professor

KU Leuven

BELGIUM

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Peng Mei

Beihang University

CHINA

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Fengshou Gu

University of Huddersfield

UNITED KINGDOM

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Enrico Zio

Politecnico di Milano

ITALY

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Yue Yu

Research Interests

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Articles (14)

MSIF-Convformer: a novel end-to-end fault diagnosis framework with multi-source sensors under strong noise

A multi-channel data fusion-enabled multilevel graph-guided framework for fault diagnosis in rotating machinery under extreme biased data

A novel multi-source sensor correlation adaptive fusion framework with uncertainty quantification for intelligent fault diagnosis

MLFNet: A novel multi-level fusion mechanical fault diagnosis network under limited and imbalanced datasets using multi-source information

From Single-Parameter Reinforcement Learning to Integrated Multi-Parameter Optimization: A Data-Driven Design Framework for Airship Aerodynamics

A novel dynamic model for magnetorheological dampers with multiscale insights into field-responsive yield transitions

A test-time adaptation method using evidential deep learning for online machinery fault diagnosis

A novel digital twin-enabled three-stage feature imputation framework for non-contact intelligent fault diagnosis

MSIFT: A novel end-to-end mechanical fault diagnosis framework under limited & imbalanced data using multi-source information fusion

A two-stage importance-aware subgraph convolutional network based on multi-source sensors for cross-domain fault diagnosis

A new multi-source information domain adaption network based on domain attributes and features transfer for cross-domain fault diagnosis

Multi-source heterogeneous information fusion fault diagnosis method based on deep neural networks under limited datasets

Multi-source domain adversarial graph convolutional networks for rolling mill health states diagnosis under variable working conditions

Rolling mill health states diagnosing method based on multi-sensor information fusion and improved DBNs under limited datasets

Collaborators (6)

Ahmet Enis Cetin

Marek Pawelczyk

PRADEEP KUNDU

Peng Mei

Fengshou Gu

Enrico Zio

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