Journal Paper: Semi-supervised Consistency Models for Automated Defect Detection in Carbon Fiber Composite Structures with Limited Data
📝 Admin 
Mar. 13, 2025 
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Our group has published a paper “Semi-supervised Consistency Models for Automated Defect Detection in Carbon Fiber Composite Structures with Limited Data” in Measurement Science and Technology introducing a novel semi-supervised approach for detecting defects in Carbon Fiber Composite Structures (CFCS), particularly focusing on ACCC wires. The paper (DOI: 10.1088/1361-6501/adc031) addresses a critical challenge in the non-destructive testing industry: the scarcity of annotated failure data for training deep learning models.
The proposed methodology combines synthetic data generation using consistency strategies, transformer-based feature fusion, and a DenseNet-based detection module. Our experiments demonstrate significant improvements in defect detection accuracy through this hybrid approach of synthetic and real-world data, making deep learning-based inspection systems more practical for industrial applications with limited training data availability.
亮点工作·工学院青年教师风采 | 陈鹏——工业装备物联网智能监测与诊断
📝 Admin Mar. 13, 2025 Comments
转引“工学院青年教师风采报道”：🖇️ 原文[链接]、[PDF]
亮点工作 | 聚焦工业装备智能监测与诊断领域，攻克工业物联网场景下多源干扰、传输失真及复杂工况带来的技术挑战。创新性地提出抗失真在线监测体系突破无线感知网络中的传输衰落与设备泛化瓶颈，开发多模态适应性诊断框架，实现风电变工况下的干扰特征解耦与噪声主动抑制，构建尺度感知迁移学习模型，攻克关键部件复杂表面损伤检测的跨域适配难题，相关成果形成覆盖信号采集-特征解析-决策优化的全链路监测方案，为高端装备可靠性提升提供全新途径。
Journal Paper: Adaptive signal regime for identifying transient shifts: A novel approach toward fault diagnosis in wind turbine systems
📝 Admin Feb. 24, 2025 Comments
We have developed an adaptive signal processing technique that enhances fault detection in offshore wind turbines, detailed in our new paper “Adaptive signal regime for identifying transient shifts: A novel approach toward fault diagnosis in wind turbine systems, (DOI: 10.1016/j.oceaneng.2025.120798) ” has been published in the prestigious Ocean Engineering journal (IF (2025) = 4.6, Top Journal).
This research addresses wind turbine bearing fault detection challenges caused by variable operating conditions and environmental noise through an adaptive signal processing framework combining real-time transient shift tracking, a Dynamic Markov Transition Frequency model, and a Multi-Period Weighted Average Framework. Validated with operational turbine data, the method achieves superior detection accuracy in non-Gaussian/transient noise environments compared to conventional approaches.
Journal Paper: Interference Suppression of Non-stationary Signals for Bearing Diagnosis under Transient Noise Measurements
📝 Admin Jan. 07, 2025 Comments
We are delighted to announce our latest research paper: “Interference Suppression of Non-stationary Signals for Bearing Diagnosis under Transient Noise Measurements, (DOI: 10.1109/TR.2025.3527739) ” has been published in the prestigious IEEE Transactions on Reliability journal (IF (2024) = 5.0, Top Journal).
This publication represents a significant contribution to the field of signal processing and bearing diagnostics, offering new approaches for noise suppression in challenging measurement conditions. Congratulations to all contributors on this outstanding achievement!
Journal Paper: Step-wise contrastive representation learning for diagnosing unknown defective categories in planetary gearboxes
📝 Admin Dec. 10, 2024 Comments
Exciting Research Breakthrough! 🎉 🎉 🎉 We have achieved a breakthrough in planetary gearbox fault detection. Our latest research, “Step-wise contrastive representation learning for diagnosing unknown defective categories in planetary gearboxes, (DOI: 10.1016/j.knosys.2024.112863) ” has been published in the prestigious Knowledge-Based Systems journal (IF (2024) = 7.2, Top Journal).
The innovative research introduces a novel approach to identifying unknown defects in planetary gearboxes, potentially revolutionizing predictive maintenance in industrial applications. This publication marks a notable contribution to the field of intelligent diagnostic systems.
Journal Paper: Scale-aware Domain Adaptation for Surface Defects Detection on Machine Tool Components in Contaminant Measurements
📝 Admin Oct. 28, 2024 Comments
Exciting news from our research lab! We’ve just published our research work “Scale-aware Domain Adaptation for Surface Defects Detection on Machine Tool Components in Contaminant Measurements“ (DOI: 10.1109/TIM.2024.3502888) in IEEE Transactions on Instrumentation and Measurement (IF (2024) = 5.6, Top Journal), one of the top journals in our field. Our research introduces a smart new technology called Scale-aware Domain Adaptation (SADA) that’s like giving machines super-vision to spot defects on industrial equipment, even when parts are covered in oil or other contaminants. This innovation is particularly useful for detecting damage on ball screws in CNC machines, making industrial maintenance more reliable and efficient.
S3DP-Lab Welcomes New Researchers for Autumn 2024
📝 Admin Oct. 19, 2024 Comments
The S3DP-Lab is delighted to welcome three new researchers for 2024: Mr. Yazheng Wang, Mr. Qingsheng Wei, and Mr. Enyu Yang. These talented individuals join our team to contribute to ongoing projects in smart sensing, signal processing, and deep learning. Their join strengthens our research capabilities and we look forward to their valuable contributions to our lab’s mission.
2024 Global Reliability & Prognostics and Health Management (GlobalRel&PHM) Conference
📝 Admin Oct. 14, 2024 Comments
Beijing, China – October 11-13, 2024: Dr. Peng Chen participated in the 2024 Global Reliability & Prognostics and Health Management (GlobalRel&PHM) Conference, held in Beijing from October 11-13. The event, co-sponsored by the IEEE Reliability Society, attracted experts from around the world to discuss advancements in reliability engineering and health management technologies.
During the conference, Dr. Chen delivered a presentation on “Markov Latent Frequency Transition Analysis for Robust Bearing Diagnosis in Transient Noise Scenarios.” His insightful talk focused on innovative methods to enhance diagnostic capabilities and reliability of bearings in challenging environments, a critical component in various industrial applications.
The GlobalRel&PHM Conference continues to serve as a premier platform for sharing cutting-edge research and fostering collaboration among professionals in the field.
Chaojun Xu Celebrates Master of Science Graduation
📝 Admin June 20, 2024 Comments
Congratulations to Chaojun Xu, who recently graduated with a Master of Science (M.S.) degree from S. T. Univ. Known for his dedication and academic excellence, Chaojun completed his studies with distinction, earning high praise for his research.
The graduation ceremony was a joyous event, with family, friends, and faculty celebrating Chaojun’s hard work and achievements. In his speech, Chaojun expressed gratitude to his supporters and shared his excitement for future endeavors.
We wish Chaojun Xu continued success in his future pursuits!
Visiting the University of Oxford’s Computer Science Department
📝 Admin June 14, 2024 Comments
S3DP-Lab Members Enhance Team Spirit in N. A. Island Retreat
📝 Admin May 24, 2024 Comments
S3DP-Lab members successfully concluded a two-day team-building event on an island in N. A. The retreat aimed to foster collaboration and strengthen the bonds among lab members through a series of structured activities and workshops. Participants engaged in team challenges, professional development sessions, and recreational activities, all designed to enhance teamwork and communication skills. The event’s scenic location and well-planned itinerary ensured that lab members returned with renewed motivation and a stronger sense of camaraderie.
Journal Paper: Self-supervised transfer learning for remote wear evaluation in machine tool elements with imaging transmission attenuation
📝 Admin Mar. 26, 2024 Comments
We are pleased to announce the publication of our paper “Self-supervised transfer learning for remote wear evaluation in machine tool elements with imaging transmission attenuation“(DOI: 10.1109/JIOT.2024.3382878) in IEEE Internet of Things Journal (IF (2023) = 10.6, Top Journal). This research proposes a self-supervised transfer learning model integrated with image capture technology, revolutionizing remote wear evaluation for CNC machines by eliminating the reliance on extensive historical data and ensuring reliability despite compromised data transmission environments.
Congratulations to Mr. Chaojun Xu for Successfully Defending His Master’s Thesis
📝 Admin May 22, 2024 Comments
We are pleased to announce that Mr. Chaojun Xu has successfully defended his master’s thesis on May 22, 2024. His thesis, titled “Toward Fault Diagnosis of Wind Turbine Gearbox via Diffusion Representation and Scale Sensing Stack Modeling,” presents groundbreaking research in the field of renewable energy technology.
Mr. Xu’s work focuses on enhancing the reliability and efficiency of wind turbines through advanced fault diagnosis techniques. His innovative approach combines diffusion representation and scale sensing stack modeling to accurately detect and diagnose faults in wind turbine gearboxes, potentially leading to significant improvements in the maintenance and operation of wind turbines.
We extend our heartfelt congratulations to Mr. Xu on this significant achievement and wish him continued success in his future endeavors.
TEPEN2024-IWFDP: International Workshop on Fault Diagnostics and Prognostics
📝 Admin May 11, 2024 Comments
Qingdao, China, May 8th-11th, 2024: Dr. Peng Chen chaired a session on “Artificial Intelligence and Machine Learning” (Certificate of Session Chair) at the international conference in Qingdao. He presented research on “Semi-Supervised Gearbox Diagnostics via Few-Shot Learning with DGAT Algorithm, ” as well as on remote intelligent diagnosis.
Journal Paper: Markov Modeling of Signal Condition Transitions for Bearing Diagnostics under External Interference Conditions
📝 Admin Feb. 15, 2024 Comments
We are delighted to announce the publication of our paper “Markov Modeling of Signal Condition Transitions for Bearing Diagnostics under External Interference Conditions“(DOI: 10.1109/TIM.2024.3383497) in IEEE Transactions on Instrumentation and Measurement (IF (2023) = 5.6, Top Journal). This innovative method enhances rolling bearing fault diagnosis by mitigating transient noise interference, improving demodulation band selection accuracy. The study’s results validate its effectiveness in identifying fault-characteristic bands, showcasing the quality of research from our lab.
New Researchers Join Our Team for Autumn 2023
Admin Oct. 27, 2023 Comments
The Autumn 2023 semester brings a wave of fresh talent to our research group, enhancing our capabilities and expanding our horizons. Our new researchers, experts in a variety of fields, are set to invigorate our projects and foster interdisciplinary collaboration. As we embark on this exciting journey with our new team members, we anticipate a season of exploration, discovery, and academic achievement. We invite you to stay tuned for updates on our research group’s activities as we continue to push the boundaries of knowledge.
Congratulations to Outstanding Students on Masters Scholarships for the 2022-2023 academic year!
Admin Oct. 19, 2023 Comments
In a remarkable achievement, three outstanding students, Mr. Chaojun Xu, Mr. Jia Gao, and Mr. Ruijin Zhang, have secured the prestigious First-Class Master’s Scholarships for the 2022-2023 academic year. Additionally, Mr. Zhigang Ma, Mr. Junxiao Ma, and Mr. Yuhao Wu have been awarded Second-Class Master’s Scholarships, demonstrating their exceptional dedication to academic excellence. Congratulations to these scholars on their well-deserved honors!
Dr. Peng Chen Fosters Collaborative Discussions at Cambridge University’s Department of Engineering Admin June 17, 2023 Comments
Seminar on Innovation and Application of Heavy Duty Mobile Robots
📝 Admin May 30, 2023 Comments
Dr. Peng Chen, accompanied by Mr. Chaojun Xu and Mr. Zhigang Ma, attended the Seminar on Innovation and Application of Heavy Duty Mobile Robots at the Foshan Institute of Intelligent Equipment Technology. This seminar, which took place on May 30, 2023, served as a platform for industry leaders, researchers, and enthusiasts to delve into the latest advancements in heavy-duty robotics and their practical applications. Participants had the opportunity to engage in idea exchange, networking, and gain valuable insights into the potential of heavy-duty robots.
Journal Paper: A Mixed Samples-driven Methodology based on Denoising Diffusion Probabilistic Model for Identifying Damage in Carbon Fiber Composite Structures
📝 Admin Apr. 16, 2023 Comments
We are thrilled to announce that our research lab has recently published a journal paper titled “A Mixed Samples-driven Methodology based on Denoising Diffusion Probabilistic Model for Identifying Damage in Carbon Fiber Composite Structures” (doi: 10.1109/TIM.2023.3267522) in the prestigious IEEE Transactions on Instrumentation and Measurement (IF (2022) = 5.33).
We would like to extend our heartfelt congratulations to the entire team of researchers involved in this project, whose dedication and hard work have made this achievement possible. This publication is a testament to the outstanding quality of research being conducted in our lab and will undoubtedly contribute to the advancement of the specific field. This achievement is a testament to the exceptional research being conducted in our lab, and we look forward to further contributions to the field.
New Researchers Join Our Lab to Drive Innovation
📝Admin Apr. 06, 2023 Comments
We are pleased to announce the newest members of our research lab. Mr. Junxiao MA, Mr. Ruijin ZHANG, Mr. Jia GAO, and Mr. Yuhao WU have recently joined our team, bringing with them fresh perspectives and valuable skills.
We believe that the addition of these four individuals will further enhance our capabilities and enable us to achieve even greater success in our ongoing research projects. Please join us in welcoming Mr. Junxiao MA, Mr. Ruijin ZHANG, Mr. Jia GAO, and Mr. Yuhao WU to our research lab. We are excited to have them on board and look forward to working with them to advance our research goals.