A5100701121- Machine Fault Diagnosis Techniques
- Risk and Safety Analysis
- Fault Detection and Control Systems
- Occupational Health and Safety Research
- Gear and Bearing Dynamics Analysis
- Reliability and Maintenance Optimization
- Engineering Diagnostics and Reliability
- Advanced Decision-Making Techniques
- Advanced Vision and Imaging
- Computer Graphics and Visualization Techniques
- Evaluation and Optimization Models
- Software Reliability and Analysis Research
- Mechanical Failure Analysis and Simulation
- Industrial Vision Systems and Defect Detection
- Air Traffic Management and Optimization
- Technology Assessment and Management
- 3D Shape Modeling and Analysis
- Air Quality Monitoring and Forecasting
- Non-Destructive Testing Techniques
- AI-based Problem Solving and Planning
- Advanced Computational Techniques and Applications
- Manufacturing Process and Optimization
- Industrial Technology and Control Systems
- Advanced Sensor Technologies Research
- Advanced Measurement and Detection Methods
China Railway Signal & Communication (China)
2025
Nanjing University of Aeronautics and Astronautics
2014-2025
Numerical Method (China)
2020
Xi'an Institute of Optics and Precision Mechanics
2020
Institute of Applied Physics and Computational Mathematics
2011-2020
Russian State University for Physical Education Sport Youth and Tourism
2020
National Natural Science Foundation of China
2018
Kunming Institute of Zoology
2013
Chinese Academy of Sciences
2013
University of Hong Kong
2013
ABSTRACT The reliability of the catenary system is crucial for safety and efficiency heavy‐haul railways. This study presents a probabilistic risk analysis model system, employing causal inference methods to capture complex relationships among factors. Using historical operational data, we identify key contributors such as environmental conditions, vehicular loads, equipment failures. By combining fault tree (FTA) failure mode effects (FMEA), establish propagation pathways. proposed method...
Abstract To address the challenges of accurately diagnosing few-shot fault samples obtained from rolling bearings under variable operating conditions, as well issues black box nature and delayed feedback to guide handling in intelligent diagnostic models, this paper proposes an interpretable multi-domain meta-transfer learning method. Firstly, vibration monitoring data different conditions are collected, time–frequency domain features extracted construct multi-channel one-dimensional...
Rolling bearings are the vital components of rotary machines. The collected data rolling bearing have strong noise interference, massive unlabeled samples, and different fault features. Thus, a deep transfer learning method is proposed for diagnosis under variable operating conditions. To obtain robust feature representation, denoising autoencoder used to denoise reduce dimension signals. For those target domain signals, matching based on multi-kernel maximum mean discrepancies between...
In real engineering scenarios, it is difficult to collect adequate cases with faulty conditions train an intelligent diagnosis system. To alleviate the problem of limited fault data, this paper proposes a method combining generative adversarial network (GAN) and stacked denoising auto-encoder (SDAE). The GAN approach augments measured especially in conditions. generated data are then transformed into SDAE model. GAN-SDAE improves accuracy from vibration signals, when samples few. usefulness...
Abstract Implementing condition monitoring and fault diagnosis of aero-engine bearings is crucial to ensure that aircraft operate safely reliably. In engineering practice, the data for are extremely limited. However, traditional methods have two shortcomings under small sample conditions: (1) they limited diagnostic performance generalization ability, (2) do not mine information sufficiently or efficiently. This article proposes a Siamese multiscale residual feature fusion network (SMSRFFN)...
Abstract As Chinese domestic civil aircraft are in the early stages of operation, accumulated operational experience is limited. Maintenance outside Structure Repair Manual (OSRM) therefore frequently encountered during structural maintenance, which significantly impacts safety and economic viability operations. To address this challenge, study proposes an OSRM maintenance decision model based on improved fuzzy comprehensive evaluation, makes full use historical cases from similar models to...
To solve the damage repair problem of civil aircraft structures beyond scope Structural Repair Manual (SRM), a decision method for structural maintenance (CASM) based on case-based reasoning (CBR) is proposed. The model fully considers practical engineering significance, realizes reuse knowledge CASM cases, and provides reference designing programs. Firstly, an ontology cases established, which unified framework managing knowledge. Secondly, CBR used to most similar historical generate...
To realize the comprehensive intelligent upgrade of Three Gorges Dam safety monitoring system (IMS), we focus on three core pillars real‐time information processing, professional analytical evaluation, and digital management control systematically overcoming critical technical bottlenecks. By deeply integrating artificial intelligence (AI), Internet Things (IOT), big data analysis, geographic + building modeling (GIS BIM) ecosystems, conducted a holistic diagnosis existing systems to...