A5102729596- Machine Fault Diagnosis Techniques
- Fault Detection and Control Systems
- Gear and Bearing Dynamics Analysis
- Engineering Diagnostics and Reliability
- Non-Destructive Testing Techniques
- Advanced machining processes and optimization
- Anomaly Detection Techniques and Applications
- Industrial Vision Systems and Defect Detection
- Structural Health Monitoring Techniques
- Mechanical Failure Analysis and Simulation
- Imbalanced Data Classification Techniques
- Domain Adaptation and Few-Shot Learning
- Reliability and Maintenance Optimization
- Advanced Measurement and Metrology Techniques
- Oil and Gas Production Techniques
- Structural Integrity and Reliability Analysis
- Ultrasonics and Acoustic Wave Propagation
- Welding Techniques and Residual Stresses
- Privacy-Preserving Technologies in Data
- Infrastructure Maintenance and Monitoring
- Tribology and Lubrication Engineering
- Artificial Intelligence in Healthcare
- Surface Roughness and Optical Measurements
- Mechanical stress and fatigue analysis
- Drilling and Well Engineering
Guangdong University of Technology
2024-2025
South China University of Technology
2013-2025
Guangzhou Experimental Station
2021-2023
Beijing Information Science & Technology University
2021-2023
KU Leuven
2019
To assess health conditions of rotating machinery efficiently, multiple accelerometers are mounted on different locations to acquire a variety possible faults signals. The statistical features extracted from these signals identify the running status machine. However, acquired vibration due sensor's arrangement and environmental interference, which may lead diagnostic results. In order improve fault diagnosis reliability, new multisensor data fusion technique is proposed. First, time-domain...
Deep neural networks present very competitive results in mechanical fault diagnosis. However, training deep models require high computing power while the performance of architectures extracting discriminative features for decision making often suffers from lack sufficient data. In this paper, a transferable convolutional network (CNN) is proposed to improve learning target tasks. First, one-dimensional CNN constructed and pretrained based on large source task datasets. Then transfer strategy...
Recently, deep learning-based intelligent fault diagnosis techniques have obtained good classification performance with amount of supervised training data. However, domain shift problem between the and testing data usually occurs due to variation in operating conditions interferences environment noise. Transfer learning provides a promising tool for handling cross-domain problems by leveraging knowledge from source help target domain. Most existing studies attempt learn both features common...
Recently, domain adaptation techniques have achieved great attention in solving domain-shift problems of mechanical fault diagnosis. However, existing methods mostly work under assumption that source and target share identical label spaces, which fail to handle those issues, where a large set data classes are available only cover subset classes. To address this problem, novel weighted adversarial transfer network (WATN) is proposed for partial diagnosis, article. Adversarial training...
In recent years, deep learning has become a promising tool for rotary machinery fault diagnosis, but it works well only when testing samples and training are independent identically distributed. practice, usually under variable speed. The change of speed leads to the variation samples' distribution, which can significantly decrease performance model. Scholars try utilize transfer techniques solving this problem. However, most exiting methods just work target instead all speed, while always...
In real industries, there often exist application scenarios where the target domain holds fault categories never observed in source domain, which is an open-set adaptation (DA) diagnosis issue. Existing DA methods under assumption of sharing identical label space across domains fail to work. What more, labeled samples can be collected from different sources, multisource information fusion rarely considered. To handle this issue, a approach developed. Specifically, data operation conditions...
Generally, high performance of deep learning (DL)-based machinery fault diagnosis methods relies on abundant labeled samples under various working conditions, while they are usually stored by different users and not communicated with each other due to data privacy protection. Federated (FL) is a possible solution, but the traditional federated averaging (FedAvg) algorithm in FL ignores potential domain shift participants, which limits its further application. Therefore, transfer framework...
Compound fault, as a primary failure leading to unexpected downtime of rotating machinery, dramatically increases the difficulty in fault diagnosis. To deal with encountered implementing compound diagnosis (CFD), researchers and engineers from industry academia have made numerous significant breakthroughs recent years. Admittedly, many systematic surveys focused on been conducted by reputable researchers. Nevertheless, previous review articles paid more attention several single or...
Remaining useful life (RUL) prediction of rolling bearings is great importance in improving the reliability and durability rotating machinery. This paper proposes a dual-attention-based convolutional neural network with accurate stage division for RUL prediction, which includes two subsections, i.e., First time (FPT) determination estimation. Firstly, signal features characterizing bearing degradation process are fused by Wasserstein Distance to perform robustness. The correct labeled...
The development of Internet Things technology provides abundant data resources for prognostics health management industrial machinery, and data-driven methods have shown their powerful ability in the field fault diagnosis. However, these several limitations: 1) Using less labeled to obtain higher accuracy is a challenging task, which limits application diagnostic models practical applications. 2) Physics-informed knowledge largely ignored during modeling process, contains wealth information...