Article
Energy & Fuels
Conor McKinnon, James Carroll, Alasdair McDonald, Sofia Koukoura, Charlie Plumley
Summary: The study utilizes the Isolation Forest Machine Learning model along with SCADA system data to monitor the condition of wind turbine pitch systems, enabling early prediction of potential faults and improving the scheduling of planned maintenance for pitch systems.
Article
Automation & Control Systems
Xingchen Liu, Juan Du, Zhi-Sheng Ye
Summary: This article develops a novel condition monitoring and fault isolation system for wind turbines based on SCADA data. The article addresses challenges such as low sampling rate, time-varying working conditions, and lack of historical fault data. The system uses preprocessing and a global monitoring statistic to monitor the health status of the wind turbine and isolate faults without expert knowledge or historical data.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Engineering, Electrical & Electronic
Changgen Li, Liang Guo, Hongli Gao, Yi Li
Summary: Anomalies in machine monitoring data can be introduced by rough environment or unexpected accidents, reducing data quality and leading to incorrect recognition of machine health status. Existing anomaly detection methods are not directly applicable to MMD, hence the introduction of a robust method called SM-iForest. Through experiments, it was found that SM-iForest can effectively detect various types of anomalies with high detection rate and low false alarm rate.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Engineering, Marine
Hailin Zheng, Qinyou Hu, Chun Yang, Qiang Mei, Peng Wang, Kelong Li
Summary: Outliers of ship trajectory affect maritime situation awareness, especially for regular ships mixed with spoofing ships. Through trajectory feature mining, differences between regular and spoofing ship trajectories were discovered. By using the isolation forest and trajectory segmentation based on time intervals, accurate identification of regular and spoofing ship trajectories can be achieved. Experimental results show that the average accuracy of identifying spoofing ships using isolation forest ranges from 88.4% to 93.3% based on different time intervals.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
Article
Environmental Sciences
Khaldoun Rishmawi, Chengquan Huang, Xiwu Zhan
Summary: Accurate information on global forest distribution and 3D structure is crucial for assessing forest biomass stocks and future terrestrial Carbon sink projections. The GEDI LiDAR sensor provides unprecedented sampling of forest structural properties, with VIIRS data successfully extrapolating GEDI measurements for wall-to-wall forest structure maps in the conterminous US. Validation results demonstrate the robustness of the VIIRS data for monitoring forest structural changes over large areas.
Article
Thermodynamics
Chang Wang, Jianqin Zheng, Yongtu Liang, Bohong Wang, Jirf Jaromfr Klemes, Zhu Zhu, Qi Liao
Summary: This article proposes an intelligent monitoring framework for operating conditions, which extracts temporal and spatial characteristics of condition samples and achieves the recognition of state changes and abnormal conditions. The framework shows high accuracy and precision in recognition results.
Article
Computer Science, Information Systems
Lishu Wang, Shuhan Jia, Xihui Yan, Libo Ma, Junlong Fang
Summary: A wind turbine generator condition monitoring framework based on the fusion of cascaded SAE abnormal condition monitoring and LightGBM abnormal condition classification is proposed. The framework has high anomaly recognition capability and fast training speed.
Article
Computer Science, Information Systems
Zhuping Zou, Yulai Xie, Kai Huang, Gongming Xu, Dan Feng, Darrell Long
Summary: This paper proposes an online container anomaly detection system based on the optimized isolation forest algorithm, which monitors and analyzes the resource metrics of containers. The system can identify abnormal resource metrics, adjust the monitoring period, and locate the cause of anomalies through analyzing container logs. Experimental results demonstrate the performance and efficiency of the system in detecting typical anomalies in containers.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2022)
Article
Energy & Fuels
Dimitris A. Barkas, Ioannis Chronis, Constantinos Psomopoulos
Summary: The causes of failure of critical components in the electrical power distribution network, such as power transformers, have always been of interest to the scientific and technical community due to their significant impact on consumers. A reliable and consistent maintenance schedule is crucial for predicting and preventing unexpected shutdowns of power transformers. This article discusses the susceptible parts of oil-immersed power transformers and their relationship with dissolved gases in the insulating oil, as well as the specific measurements and maintenance actions that should be implemented.
Article
Engineering, Mechanical
Xiaomeng Li, Yi Wang, Guangyao Zhang, Baoping Tang, Yi Qin
Summary: Inspired by chaos fractal theory and slowly varying damage dynamics theory, this paper proposes a new health monitoring indicator for vibration signals of rotating machinery, which can effectively monitor the mechanical condition under both cyclo-stationary and variable operating conditions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Industrial
Haoxuan Zhou, Bingsen Wang, Enrico Zio, Guangrui Wen, Zimin Liu, Yu Su, Xuefeng Chen
Summary: This paper proposes a novel method for dealing with time-varying operating conditions (TVOCs) in condition monitoring (CM). The method is based on a neural network and a state-space model (SSM) to build a hybrid system response model for describing the operating process of equipment under TVOCs. Experiments on accelerated fatigue degradation of bearings validate the effectiveness and superiority of the proposed method, as it eliminates the interference of TVOCs and constructs an effective health indicator (HI).
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Computer Science, Interdisciplinary Applications
Marcos Leandro Hoffmann Souza, Cristiano Andre da Costa, Gabriel de Oliveira Ramos, Rodrigo da Rosa Righi
Summary: This study proposes a new approach to improve the reliability of production systems by combining data mining techniques with asset health management, using semi-supervised machine learning technology and sensor data. The efficacy of the approach was validated through a case study in a styrene petrochemical plant.
COMPUTERS IN INDUSTRY
(2021)
Article
Engineering, Chemical
Philipp Zurcher, Sara Badr, Stephanie Knuppel, Hirokazu Sugiyama
Summary: This paper proposes a methodology to infer equipment condition information using existing process monitoring infrastructure, and verifies its application value through case studies. The successful application of this method provides lessons-learned for including maintenance as part of holistic system design and achieves data-driven equipment reliability assessment and maintenance planning in pharmaceutical manufacturing.
CHEMICAL ENGINEERING RESEARCH & DESIGN
(2022)
Article
Soil Science
Raul Roberto Poppiel, Jose Alexandre Melo Dematte, Nicolas Augusto Rosin, Lucas Rabelo Campos, Mahboobeh Tayebi, Benito Roberto Bonfatti, Shamsollah Ayoubi, Samaneh Tajik, Farideh Abbaszadeh Afshar, Azam Jafari, Nikou Hamzehpour, Ruhollah Taghizadeh-Mehrjardi, Yaser Ostovari, Najmeh Asgari, Salman Naimi, Kamal Nabiollahi, Hassan Fathizad, Mojtaba Zeraatpisheh, Fatemeh Javaheri, Maryam Doustaky, Mehdi Naderi, Somayeh Dehghani, Saeedeh Atash, Akram Farshadirad, Salman Mirzaee, Ali Shahriari, Maryam Ghorbani, Mehdi Rahmati
Summary: This study utilized random forest algorithm and remote sensing data to generate a topsoil map covering nearly 3.34 million square kilometers in the Middle East, based on data from over 5000 sites. The findings revealed that topsoils in the Middle East are characterized by sandy and loamy textures, low organic carbon levels, alkaline reactions, and high calcium carbonate content.
Article
Green & Sustainable Science & Technology
Xiaohang Jin, Zhuangwei Xu, Wei Qiao
Summary: This article proposes an ensemble approach to detect anomalies and diagnose faults in wind turbines, based on modeling and analyzing historical SCADA data from healthy wind turbines. The method can detect anomalies and diagnose faults before wind turbines have to be shut down for maintenance.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2021)
Article
Computer Science, Artificial Intelligence
Hongzhan Ma, Xuening Chu, Deyi Xue, Dongping Chen
JOURNAL OF INTELLIGENT MANUFACTURING
(2019)
Article
Engineering, Industrial
Lei Zhang, Xuening Chu, Hansi Chen, Bo Yan
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2019)
Article
Engineering, Industrial
Lei Zhang, Xuening Chu, Deyi Xue
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2019)
Article
Computer Science, Artificial Intelligence
Hongzhan Ma, Xuening Chu, Yupeng Li
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2019)
Article
Engineering, Industrial
Zheng Wang, Hongzhan Ma, Hansi Chen, Bo Yan, Xuening Chu
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2020)
Article
Computer Science, Artificial Intelligence
Hongzhan Ma, Xuening Chu, Weizhong Wang, Xinwang Liu, Deyi Xue
ADVANCED ENGINEERING INFORMATICS
(2019)
Article
Computer Science, Interdisciplinary Applications
Binbin Ma, Hongzhan Ma, Yupeng Li, Xuening Chu, Qingxiu Liu
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
(2019)
Article
Computer Science, Artificial Intelligence
Hansi Chen, Lei Zhang, Xuening Chu, Bo Yan
ADVANCED ENGINEERING INFORMATICS
(2019)
Article
Computer Science, Interdisciplinary Applications
Hang Liu, Xuening Chu, Deyi Xue
COMPUTERS IN INDUSTRY
(2019)
Article
Computer Science, Artificial Intelligence
Hansi Chen, Hang Liu, Xuening Chu, Lei Zhang, Bo Yan
ADVANCED ENGINEERING INFORMATICS
(2020)
Article
Engineering, Industrial
Zheng Wang, Qingxiu Liu, Hansi Chen, Xuening Chu
Summary: A new fault diagnosis method utilizing deformable convolutional neural network (CNN), deep long short-term memory (DLSTM) and transfer learning strategies was designed for rolling bearing fault diagnosis. Experimental results showed superior performance of the proposed model compared to state-of-the-art methods in identifying fault types of bearings.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Zhenhua Liu, Mengting Zhang, Yupeng Li, Xuening Chu
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2020)
Article
Computer Science, Artificial Intelligence
Zhihua Zhao, Yupeng Li, Xuening Chu
Summary: This study proposes a data-driven approach for identifying obsolete functions in order to improve design. By constructing observing parameters of functional performance and defining desired levels of performance, along with utilizing time series and obsolescence index, obsolete functions can be effectively identified considering the evolution of customer requirements.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Green & Sustainable Science & Technology
Hansi Chen, Hang Liu, Xuening Chu, Qingxiu Liu, Deyi Xue
Summary: Continuous monitoring of wind turbine health conditions using anomaly detection methods based on LSTM and AE neural network can improve reliability and reduce maintenance costs.
Article
Computer Science, Artificial Intelligence
Fangyu Chen, Yongchang Wei, Hongchang Ji, Gangyan Xu
Summary: This paper introduces a dual-layer network analytical framework for evaluating standard systems in construction safety management and validates its effectiveness through a case study. The research findings suggest that key standards often encompass a wider array of risks, providing suggestions for revising construction standards.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Minghao Li, Qiubing Ren, Mingchao Li, Ting Kong, Heng Li, Huijing Tian, Shiyuan Liu
Summary: This study proposes a method using digital twin technology to construct a collision early warning system for marine piling. The system utilizes a five-dimensional model and four independently maintainable development modules to maximize its effectiveness. The pile positioning algorithm and collision early warning algorithm are capable of providing warnings for complex pile groups.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Seokhyun Ryu, Sungjoo Lee
Summary: This study proposes the use of patent information to develop a robust technology tree and applies it to the furniture manufacturing process. Through methods such as clustering analysis, semantic analysis, and association-rule mining, technological attributes and their relationships are extracted and analyzed. This approach provides meaningful information to improve the understanding of a target technology and supports research and development planning.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Shuai Ma, Kechen Song, Menghui Niu, Hongkun Tian, Yanyan Wang, Yunhui Yan
Summary: This paper proposes a feature-based domain disentanglement and randomization (FDDR) framework to improve the generalization of deep models in unseen datasets. The framework successfully addresses the appearance difference issue between training and test images by decomposing the defect image into domain-invariant structural features and domain-specific style features. It also utilizes randomly generated samples for training to further expand the training sample.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Fang Xu, Tianyu Zhou, Hengxu You, Jing Du
Summary: This study explores the impact of AR-based egocentric perspectives on indoor wayfinding performance. The results reveal that participants using the egocentric perspective demonstrate improved efficiency, reduced cognitive load, and enhanced spatial awareness in indoor navigation tasks.
ADVANCED ENGINEERING INFORMATICS
(2024)
Review
Computer Science, Artificial Intelligence
Yujie Lu, Shuo Wang, Sensen Fan, Jiahui Lu, Peixian Li, Pingbo Tang
Summary: Image-based 3D reconstruction plays a crucial role in civil engineering by bridging the gap between physical objects and as-built models. This study provides a comprehensive summary of the field over the past decade, highlighting its interdisciplinary nature and integration of various technologies such as photogrammetry, 3D point cloud analysis, semantic segmentation, and deep learning. The proposed 3D reconstruction knowledge framework outlines the essential elements, use phases, and reconstruction scales, and identifies eight future research directions. This review is valuable for scholars interested in the current state and future trends of image-based 3D reconstruction in civil engineering, particularly in relation to deep learning methods.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Hang Zhang, Wenhu Wang, Shusheng Zhang, Yajun Zhang, Jingtao Zhou, Zhen Wang, Bo Huang, Rui Huang
Summary: This paper presents a novel framework for segmenting intersecting machining features using deep reinforcement learning. The framework enhances the effectiveness of intersecting machining feature segmentation by leveraging the robust feature representation, decision-making, and automatic learning capabilities of deep reinforcement learning. Experimental results demonstrate that the proposed approach successfully addresses some existing challenges faced by several state-of-the-art methods in intersecting machining feature segmentation.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Chao Zhao, Weiming Shen
Summary: This paper proposes a semantic-discriminative augmentation-driven network for imbalanced domain generalization fault diagnosis, which enhances the model's generalization capabilities through synthesizing reliable samples and optimizing representations.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Ching-Chih Chang, Teng-Wen Chang, Hsin-Yi Huang, Shih-Ting Tsai
Summary: Ideation is the process of generating ideas through exploring visual and semantic stimuli for creative problem-solving. This process often requires changes in user goals and insights. Using pre-designed content and semantic-visual concepts for ideation can introduce uncertainty. An adaptive workflow is proposed in this study that involves extracting and summarizing semantic-visual features, using clusters of adapted information for multi-label classification, and constructing a design exploration model with visualization and exploration.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Zhen Wang, Shusheng Zhang, Hang Zhang, Yajun Zhang, Jiachen Liang, Rui Huang, Bo Huang
Summary: This research proposes a novel approach for machining feature process planning using graph convolutional neural networks. By representing part information with attribute graphs and constructing a learning model, the proposed method achieves higher accuracy and resolves current limitations in machining feature process planning.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Hong-Wei Xu, Wei Qin, Jin-Hua Hu, Yan-Ning Sun, You -Long Lv, Jie Zhang
Summary: Wafer fabrication is a complex manufacturing system, where understanding the correlation between parameters is crucial for identifying the cause of wafer defects. This study proposes a Copula network deconvolution-based framework for separating direct correlations, which involves constructing a complex network correlation diagram and designing a nonlinear correlation metric model. The proposed method enables explainable fault detection by identifying direct correlations.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Yida Hong, Wenqiang Li, Chuanxiao Li, Hai Xiang, Sitong Ling
Summary: An adaptive push method based on feature transfer is proposed to address sparsity and cold start issues in product intelligent design. By constructing a collaborative filtering algorithm model and transforming the rating model, the method successfully alleviates data sparsity and cold start problems.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Hairui Fang, Jialin An, Bo Sun, Dongsheng Chen, Jingyu Bai, Han Liu, Jiawei Xiang, Wenjie Bai, Dong Wang, Siyuan Fan, Chuanfei Hu, Fir Dunkin, Yingjie Wu
Summary: This work proposes a model for real-time fault diagnosis and distance localization on edge computing devices, achieving lightweight design and high accuracy in complex environments. It also demonstrates a high frame rate on edge computing devices, providing a novel solution for industrial practice.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Yujun Jiao, Xukai Zhai, Luyajing Peng, Junkai Liu, Yang Liang, Zhishuai Yin
Summary: This paper proposes a digital twin-based motion forecasting framework that predicts the future trajectories of workers on construction sites, accurately predicting workers' motions in potential risk scenarios.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Ling-Zhe Zhang, Xiang-Dong Huang, Yan-Kai Wang, Jia-Lin Qiao, Shao-Xu Song, Jian-Min Wang
Summary: Time-series DBMSs based on the LSM-tree have been widely applied in various scenarios. The characteristics of time-series data workload pose challenges to efficient queries. To address issues like query latency and inaccurate range, we propose a novel compaction algorithm called Time-Tiered Compaction.
ADVANCED ENGINEERING INFORMATICS
(2024)