Lecturer at University of Toronto, China
Dr. Xiaoxia Yu is a Lecturer in the School of Mechanical Engineering at Chongqing University of Technology, with a strong research background in mechanical fault diagnosis, wind turbine health assessment, and advanced neural networks. A dedicated scholar and innovator, Dr. Yu earned her Ph.D. in Mechanical Engineering from Chongqing University in 2023, following her Master’s and Bachelor’s degrees from the same university in Vehicle Engineering and Armored Vehicle Engineering respectively. Her work bridges theory and application, focusing on the development of digital twin systems and graph neural networks to enhance the performance and reliability of large-scale rotating machinery, especially in the wind power sector. She has authored high-impact papers in top journals such as Renewable Energy, Mechanical Systems and Signal Processing, and IEEE Transactions on Instrumentation and Measurement, frequently serving as the first and corresponding author. In addition to scholarly writing, Dr. Yu has secured major research grants from the Chongqing Education Commission and Science and Technology Bureau, underscoring her leadership and technical credibility. Her patents reflect practical innovation in structural health monitoring and fault detection methods. With her interdisciplinary approach and rapidly expanding academic footprint, Dr. Yu is a rising expert in intelligent fault diagnostics and prognostics of mechanical systems.
Professional Profile
Education
Dr. Xiaoxia Yu’s educational trajectory demonstrates a consistent pursuit of excellence in mechanical and vehicle engineering. She earned her Ph.D. in Mechanical Engineering from Chongqing University (2019–2023), where she conducted cutting-edge research on the use of neural networks and graph-based methods for predicting degradation trends in wind turbine components. Her doctoral work integrated advanced computational models with real-world engineering applications, positioning her at the forefront of intelligent diagnostics. Prior to this, she obtained her Master’s degree in Vehicle Engineering (2016–2019) from Chongqing University of Technology, where she deepened her understanding of vehicle dynamics, system modeling, and mechanical performance. Her academic foundation was laid with a Bachelor’s degree in Armored Vehicle Engineering (2012–2016), also from Chongqing University of Technology. Throughout her education, Dr. Yu consistently ranked among the top students and developed a solid grounding in both mechanical theory and applied technology. The combination of her diverse yet aligned academic background enables her to approach complex mechanical problems with a systems-level understanding and data-driven solutions. Her progressive academic development has culminated in a research-driven, application-oriented engineering mindset that continues to shape her teaching and research career.
Professional Experience
Dr. Xiaoxia Yu currently serves as a Lecturer at the School of Mechanical Engineering, Chongqing University of Technology, since June 2023. In this role, she teaches undergraduate and postgraduate courses while leading independent research in intelligent maintenance and health monitoring of large-scale mechanical systems. Her prior academic experience includes active participation in a national key R&D project funded by the Ministry of Science and Technology, where she contributed to the development of a health management software system for large rotating machinery. Although she has not undertaken formal postdoctoral training, her involvement in high-impact research projects and collaboration with senior experts have allowed her to develop strong project management and research capabilities. She has independently led projects funded by the Chongqing Education Commission and the Natural Science Foundation of Chongqing, focusing on digital twin systems and graph-based neural networks for wind turbine health diagnostics. These roles have not only enhanced her academic leadership but also allowed her to supervise students and publish in high-ranking journals. Dr. Yu’s professional engagements showcase a clear commitment to solving real-world engineering challenges through rigorous research and innovation.
Research Interest
Dr. Xiaoxia Yu’s research interests lie at the intersection of intelligent diagnostics, prognostics, and advanced computational modeling for mechanical systems. She specializes in fault diagnosis and health assessment of wind turbines and other rotating machinery using artificial intelligence techniques, particularly graph neural networks, reinforcement learning, and meta-learning. A core focus of her work is on developing digital twin systems to monitor and predict the degradation of key components under complex environmental conditions, such as wave, wind, and flow loads. She is also deeply engaged in the study of small-sample learning, robust signal processing, and adaptive algorithms for intelligent maintenance. Her passion for enhancing the reliability and efficiency of renewable energy systems has guided much of her recent work on ultra-large wind turbines. By integrating mechanical knowledge with advanced data analytics, Dr. Yu is helping to drive the next generation of predictive maintenance systems. Her research not only advances theoretical modeling but also directly informs practical solutions for industrial applications, aligning with national goals in energy sustainability and smart manufacturing. Through interdisciplinary collaboration and continuous exploration, Dr. Yu aims to make impactful contributions to the field of intelligent mechanical systems.
Research Skills
Dr. Xiaoxia Yu possesses a comprehensive skill set in computational mechanics, intelligent fault diagnosis, and machine learning applications for engineering systems. Technically proficient in deep learning, graph convolutional networks (GCNs), reinforcement learning, and digital twin modeling, she applies these tools to enhance the health monitoring of wind turbines and other critical infrastructure. Her hands-on experience includes working with signal processing tools, vibration analysis, and structural health monitoring technologies. She is skilled in MATLAB, Python, and other engineering simulation platforms necessary for real-time data interpretation and diagnostics. In terms of system modeling, Dr. Yu is adept at developing lightweight and efficient neural networks tailored for edge-computing environments in industrial settings. Her patent portfolio also reflects her strength in algorithm development for fault detection, image segmentation, and structural optimization. Moreover, Dr. Yu is capable of designing and implementing experiments that simulate real-world mechanical degradation, validating her models in controlled environments. She demonstrates excellence in scientific writing and has peer-reviewed for international journals, further emphasizing her expertise. With this robust mix of theoretical knowledge and practical experience, Dr. Yu stands out as a multi-skilled researcher capable of addressing the most pressing challenges in mechanical system reliability.
Awards and Honors
Dr. Xiaoxia Yu has garnered significant recognition for her research and innovation in the field of mechanical health diagnostics. Her academic excellence is evident in her publication record, with first-author papers in leading international journals such as Renewable Energy, IEEE Transactions, and Mechanical Systems and Signal Processing. She is the recipient of multiple competitive research grants from the Chongqing Education Commission and Chongqing Science and Technology Bureau, reflecting peer recognition of her project leadership capabilities. Her patents—spanning image recognition, fault detection, and robotic control—underscore her ability to translate academic research into practical technologies. In total, she has filed and co-authored over 10 patents, several of which are already granted or under review, including international filings. These innovations have contributed to the advancement of intelligent fault detection systems for wind turbines and rotating machinery. Dr. Yu’s achievements have not only established her as a leading early-career researcher but also positioned her work to support national strategic goals in energy and industrial automation. Her success represents a balance of academic rigor, engineering application, and innovation. As a promising talent in smart manufacturing and energy systems, Dr. Yu’s contributions are well-recognized and steadily expanding.
Conclusion
In summary, Dr. Xiaoxia Yu is a rising star in the field of intelligent mechanical systems and renewable energy diagnostics. Through her academic achievements, advanced research skills, and innovative applications of AI in engineering, she has carved out a unique niche in fault detection and predictive maintenance. Her interdisciplinary background—spanning vehicle engineering, mechanical diagnostics, and computational intelligence—equips her with a rare ability to address complex mechanical challenges with practical and scalable solutions. She has shown remarkable initiative in securing research funding, publishing in top-tier journals, and translating ideas into patents. Beyond her technical prowess, Dr. Yu demonstrates leadership in academic mentorship and actively contributes to the scientific community. As China and the global industry move towards intelligent manufacturing and clean energy, professionals like Dr. Yu will be instrumental in shaping a resilient and efficient future. Her career thus far not only reflects personal excellence but also a commitment to societal advancement through science and engineering. She is poised to become a leading voice in the field of intelligent diagnostics for large-scale mechanical systems, with a promising trajectory ahead.
Publications Top Notes
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Title: Fault diagnosis of wind turbine gearbox using a novel method of fast deep graph convolutional networks
Authors: Xiaoxia Yu, B. Tang, K. Zhang
Year: 2021
Citations: 180
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Title: Fault diagnosis of rotating machinery based on graph weighted reinforcement networks under small samples and strong noise
Authors: Xiaoxia Yu, B. Tang, L. Deng
Year: 2023
Citations: 61
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Title: Fault detection of wind turbines by subspace reconstruction-based robust kernel principal component analysis
Authors: K. Zhang, B. Tang, L. Deng, Xiaoxia Yu
Year: 2021
Citations: 52
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Title: Multiscale dynamic fusion prototypical cluster network for fault diagnosis of planetary gearbox under few labeled samples
Authors: B. Li, B. Tang, L. Deng, Xiaoxia Yu
Year: 2020
Citations: 46
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Title: Multi-block domain adaptation with central moment discrepancy for fault diagnosis
Authors: P. Xiong, B. Tang, L. Deng, M. Zhao, Xiaoxia Yu
Year: 2021
Citations: 45
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Title: Fault source location of wind turbine based on heterogeneous nodes complex network
Authors: K. Zhang, B. Tang, L. Deng, Xiaoxia Yu, J. Wei
Year: 2021
Citations: 21
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Title:Fault Diagnosis Method for Aircraft Engine Accessory Gearbox under Strong Background Noise Based on Adaptive Graph Convolutional Neural Network
Authors: Xiaoxia Yu, Baoping Tang, Jing Wei, Lei Deng
Year: 2021
Citations: 9
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Title: On the calculation of the dipole correlation function for rigid rotator (under random forces)
Authors: W.T. Coffey, A. Morita
Year: 1976
Citations: 8
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Title: A multi-head self-attention autoencoder network for fault detection of wind turbine gearboxes under random loads
Authors: Xiaoxia Yu, Z. Zhang, B. Tang, M. Zhao
Year: 2024
Citations: 4
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Title: Gear degradation trend prediction by meta-learning gated recurrent unit networks under few samples
Authors: Xiaoxia Yu, Lei Deng, Baoping Tang, Yi Xia, Qikang Li
Year: 2022
Citations: 4