A5100342029- Fault Detection and Control Systems
- Machine Fault Diagnosis Techniques
- Anomaly Detection Techniques and Applications
- Neural Networks and Applications
- Engineering Diagnostics and Reliability
- Advanced Graph Neural Networks
- Fuzzy Logic and Control Systems
- Resource-Constrained Project Scheduling
- Adversarial Robustness in Machine Learning
- Rough Sets and Fuzzy Logic
- Advanced Algorithms and Applications
- Reliability and Maintenance Optimization
- Manufacturing Process and Optimization
- Advanced Measurement and Detection Methods
- Construction Project Management and Performance
- Topic Modeling
- Data Quality and Management
- Drilling and Well Engineering
- Scheduling and Optimization Algorithms
- BIM and Construction Integration
- Advanced Sensor and Control Systems
- Advanced Combustion Engine Technologies
- Advanced Computational Techniques and Applications
- Industrial Technology and Control Systems
- Efficiency Analysis Using DEA
Harbin Institute of Technology
2016-2025
Fujian Jiangxia University
2023
Shenzhen Technology University
2023
Dalian University of Technology
2006-2021
Fuzzy Systems Institute
2014-2021
University of Science and Technology of China
2021
Cangzhou Normal University
2020
Shandong Provincial Water Resources Research Institute
2020
Hunan Normal University
2019
Lawrence Berkeley National Laboratory
2019
One of the significant tasks in remaining useful life (RUL) prediction is to find a good health indicator (HI) that can effectively represent degradation process system. However, it difficult for traditional data-driven methods construct accurate HIs due their incomprehensive consideration temporal dependencies within monitoring data, especially aeroengines working under nonstationary operating conditions (OCs). Aiming at this problem, article develops novel unsupervised deep neural network,...
Due to the complexity of English machine translation technology and its broad application prospects, many experts scholars have invested more energy analyze it. In view complex changeable forms, large difference between Chinese word order, insufficient Chinese-English parallel corpus resources, this paper uses deep learning complete conversion English. The research focus is how use language pairs with rich resources improve performance neural translation, that is, multi-task train models....
Engineering data are often highly nonlinear and contain high-frequency noise, so the Levenberg–Marquardt (LM) algorithm may not converge when a neural network optimized by is trained with engineering data. In this work, we analyzed reasons for LM network’s poor convergence commonly associated algorithm. Specifically, effects of different activation functions such as Sigmoid, Tanh, Rectified Linear Unit (RELU) Parametric (PRLU) were evaluated on general performance networks, special values...
Many existing aircraft engine fault detection methods are highly dependent on performance deviation data that provided by the original equipment manufacturer. To improve independent ability, Aircraft Communications Addressing and Reporting System (ACARS) can be used. However, owing to characteristics of high dimension, complex correlations between parameters, large noise content, it is difficult for detect faults effectively using ACARS data. solve this problem, a novel method based...
Effective fault detection and identification methods are crucial in gas turbine maintenance. To express the performance of symptom state precisely to reduce individual differences different turbines, a novel deviation model based on real-life operation data turbines is proposed this paper. A backpropagation neural network adopted establish model. Performance values calculated by regarded as signatures turbines. enhance accuracy diagnosis, multikernel support vector machine employed...
Considering that large mechanical equipment often has various excitation sources, the signals generated by these sources are not simply added or multiplied together, but nonlinearly mixed, which exhibit complex non-stationary characteristics, making classical algorithms difficult to extract fault features. Especially when faults just occur, symptom is weak and submerged noise, resulting in low diagnosing accuracy. Accordingly, this article develops a new deep attention method, namely...
To achieve intelligent manufacturing and improve the machining quality of machine tools, this paper proposes an interpretable size prediction model combining eXtreme Gradient Boosting (XGBoost), autoencoder (AE), Shapley additive explanation (SHAP) analysis. In study, XGBoost was used to establish evaluation system for actual computer numerical control (CNC) tools. The combined with SHAP approximation effectively capture local global features in data using autoencoders transform preprocessed...
Monitoring gas turbines' health, in particular, detecting abnormal behaviors time, is critical ensuring turbine operating safety and preventing costly unplanned maintenance. One most popular anomaly detection method to obtain a classification-prediction model by training classifier using the real-life data of turbine. The excellent ability this attributed enough annotated samples, especially samples. Nevertheless, monitoring data, normal far more than even no data. Advanced technologies that...