1 citations · 1 across the 13 of their papers we have counts for
13 papers
Fault Diagnosis of Dynamic Systems Under Unknown Operating Conditions: A Condition-Guided Selective Adaptation Approach
Jiaming Liu, Zeyi Liu, Hongshuo Zhao +2
Fault diagnosis under unknown operating conditions remains challenging for dynamic industrial systems, as the distribution shift caused by changing operating conditions can signifi…
Predictive Relative-Velocity Steering for Safe Robotic Manipulator Teleoperation in Dynamic Environments
Changhao Hu, Zeyi Liu, Songqiao Hu +3
Recent advances in teleoperation have enabled robotic manipulators to perform dexterous, human-arm-like motions. However, human operators may fail to avoid suddenly appearing obsta…
Asymmetric Adaptation-based Real-time Fault Diagnosis Under Transitional Operating Conditions
Hongshuo Zhao, Zeyi Liu, Xiao He
Data streams in real-world industrial scenarios often contain transitional operating conditions that are uncovered during offline training, leading to significant distribution shif…
Replay-guided Test-time Adaptation for Fault Diagnosis Under Unseen Operating Conditions
Yakun Wang, Pengyu Han, Zeyi Liu +3
In modern industrial systems, machinery frequently operates under dynamic environments with continuously varying loads and speeds. Consequently, deep learning-based fault diagnosis…
Multimodal Benchmark for Safety Assessment in Industrial Inspection Scenarios
Zeyi Liu, Shuang Liu, Jihai Min +7
With the rapid development of industrial intelligence and unmanned inspection, reliable perception and safety assessment for AI systems in complex and dynamic industrial sites has…
VLSA: Vision-Language-Action Models with Plug-and-Play Safety Constraint Layer
Songqiao Hu, Zeyi Liu, Shuang Liu +5
Vision-Language-Action (VLA) models have demonstrated remarkable capabilities in generalizing across diverse robotic manipulation tasks. However, deploying these models in unstruct…
Awesome-OL: An Extensible Toolkit for Online Learning
Zeyi Liu, Songqiao Hu, Pengyu Han +2
In recent years, online learning has attracted increasing attention due to its adaptive capability to process streaming and non-stationary data. To facilitate algorithm development…
Rethinking the Role of Operating Conditions for Learning-based Multi-condition Fault Diagnosis
Pengyu Han, Zeyi Liu, Shijin Chen +2
Multi-condition fault diagnosis is prevalent in industrial systems and presents substantial challenges for conventional diagnostic approaches. The discrepancy in data distributions…
Lite-RVFL: A Lightweight Random Vector Functional-Link Neural Network for Learning Under Concept Drift
Songqiao Hu, Zeyi Liu, Xiao He
The change in data distribution over time, also known as concept drift, poses a significant challenge to the reliability of online learning methods. Existing methods typically requ…
Towards Next-Generation Intelligent Maintenance: Collaborative Fusion of Large and Small Models
Xiaoyi Yuan, Qiming Huang, Mingqing Guo +4
With the rapid advancement of intelligent technologies, collaborative frameworks integrating large and small models have emerged as a promising approach for enhancing industrial ma…
Learning-based Adaptive Safety-Critical Control With Evolving Unsafe Regions
Songqiao Hu, Zidong Wang, Zeyi Liu +2
Control barrier functions (CBFs) provide a principled framework for safety-critical control, but their construction typically requires an explicit and differentiable description of…
PB-OEL: A Performance-Bounded Online Ensemble Learning Framework With Mixed Feedback for Real-Time Safety Assessment
Songqiao Hu, Zeyi Liu, Lufeng Hao +2
Real-time safety assessment is critical for ensuring the reliable operation of complex dynamic systems. However, obtaining full safety labels in real time is often prohibitively ex…
CADM: Confusion Model-based Detection Method for Real-drift in Chunk Data Stream
Songqiao Hu, Zeyi Liu, Xiao He
Concept drift detection has attracted considerable attention due to its importance in many real-world applications such as health monitoring and fault diagnosis. Conventionally, mo…