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Qianyu Zhou
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Journal Articles

Discriminative Feature Extraction through Dictionary Learning for Piezoelectric Admittance-Based Bolt Loosening Damage Detection

Accepted Manuscript
Yang Zhang, Qianyu Zhou, Jiong Tang
Journal: ASME Letters in Dynamic Systems and Control
Publisher: ASME
Article Type: Research Papers
Letters Dyn. Sys. Control.
Paper No: ALDSC-25-1002
https://doi.org/10.1115/1.4068353
Published Online: April 2, 2025
View Articletitled, Discriminative Feature Extraction through Dictionary Learning for Piezoelectric Admittance-Based Bolt Loosening Damage Detection
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Topics: Damage, Feature extraction, Mechanical admittance, Acoustic emissions, Algorithms, Artificial neural networks, Atoms, Complex systems, Corners (Structural elements), Errors
Proceedings Papers

Leveraging Physics-Informed Transformers for Advanced Signal Compression and Reconstruction in Machinery Fault Diagnosis

Qianyu Zhou, Jiong Tang
Proc. ASME. IMECE2024, Volume 2: Advanced Manufacturing, V002T03A023, November 17–21, 2024
Publisher: American Society of Mechanical Engineers
Paper No: IMECE2024-145617
https://doi.org/10.1115/IMECE2024-145617
View Paper titled, Leveraging Physics-Informed Transformers for Advanced Signal Compression and Reconstruction in Machinery Fault Diagnosis
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Topics: Compression, Fault diagnosis, Machinery, Physics, Signals
Journal Articles

Harnessing Bayesian Deep Learning to Tackle Unseen and Uncertain Scenarios in Diagnosis of Machinery Systems

Zhou Kai, Qianyu Zhou, Jiong Tang
Journal: ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
Publisher: ASME
Article Type: Research-Article
ASME J. Risk Uncertainty Part B. March 2025, 11(1): 011106.
Paper No: RISK-24-1059
https://doi.org/10.1115/1.4067001
Published Online: November 27, 2024
View Articletitled, Harnessing Bayesian Deep Learning to Tackle Unseen and Uncertain Scenarios in Diagnosis of Machinery Systems
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Topics: Convolutional neural networks, Deep learning, Fault diagnosis, Gears, Machinery, Noise (Sound), Testing, Uncertainty, Vibration, Probability
Proceedings Papers

Spatially-Aware Milling Surface Flatness Prediction Through Physics-Based Graph Neural Network

Qianyu Zhou, Jeongho Kim, Jiong Tang
Proc. ASME. IDETC-CIE2024, Volume 2A: 44th Computers and Information in Engineering Conference (CIE), V02AT02A030, August 25–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: DETC2024-143787
https://doi.org/10.1115/DETC2024-143787
View Paper titled, Spatially-Aware Milling Surface Flatness Prediction Through Physics-Based Graph Neural Network
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Topics: Aluminum, Graph neural networks, Milling, Physics
Proceedings Papers

Efficient Fault Detection in Bearings: Synergizing Transformer Adaptations With Convolutional Kernel

Qianyu Zhou, Jiong Tang
Proc. ASME. ISFA2024, 2024 International Symposium on Flexible Automation, V001T08A008, July 21–24, 2024
Publisher: American Society of Mechanical Engineers
Paper No: ISFA2024-141122
https://doi.org/10.1115/ISFA2024-141122
View Paper titled, Efficient Fault Detection in Bearings: Synergizing Transformer Adaptations With Convolutional Kernel
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Topics: Bearings, Deep learning, Fault diagnosis, Flaw detection, Rockets
Proceedings Papers

Gearbox Fault Detection via Physics-Informed Parallel Deep Learning Model Architecture

Qianyu Zhou, Jiong Tang
Proc. ASME. IDETC-CIE2023, Volume 11: 2023 International Power Transmission and Gearing Conference (PTG), V011T11A018, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-116599
https://doi.org/10.1115/DETC2023-116599
View Paper titled, Gearbox Fault Detection via Physics-Informed Parallel Deep Learning Model Architecture
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Topics: Flaw detection, Gears, Mechanical drives, Physics, Wavelet transforms
Proceedings Papers

Part Surface Inspection Through Semi-Supervised Learning to Overcome Limited Data Challenge

Qianyu Zhou, Jiong Tang
Proc. ASME. MSEC2023, Volume 2: Manufacturing Equipment and Automation; Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T09A008, June 12–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2023-105105
https://doi.org/10.1115/MSEC2023-105105
View Paper titled, Part Surface Inspection Through Semi-Supervised Learning to Overcome Limited Data Challenge
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Topics: Geometry, Inspection