2 citations · 7 across the 17 of their papers we have counts for
17 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…
Multi-mode Fault Diagnosis Datasets of Three-phase Asynchronous Motor Under Variable Working Conditions
Shijin Chen, Zeyi Liu, Chenyang Li +3
Three-phase asynchronous motor are fundamental components in industrial systems, and their failure can lead to significant operational downtime and economic losses. Vibration and c…
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…
Multi-Condition Fault Diagnosis of Dynamic Systems: A Survey, Insights, and Prospects
Pengyu Han, Zeyi Liu, Xiao He +2
With the increasing complexity of industrial production systems, accurate fault diagnosis is essential to ensure safe and efficient system operation. However, due to changes in pro…
Multi-mode Fault Diagnosis Datasets of Gearbox Under Variable Working Conditions
Shijin Chen, Zeyi Liu, Xiao He +2
The gearbox is a critical component of electromechanical systems. The occurrence of multiple faults can significantly impact system accuracy and service life. The vibration signal…
An Evidential Real-Time Multi-Mode Fault Diagnosis Approach Based on Broad Learning System
Chen Li, Zeyi Liu, Limin Wang +2
Fault diagnosis is a crucial area of research in industry. Industrial processes exhibit diverse operating conditions, where data often have non-Gaussian, multi-mode, and center-dri…
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…