Digital Twin-driven online anomaly detection for an automation system based on edge intelligence
暂无分享,去创建一个
Yuqian Lu | Huiyue Huang | Lei Yang | Yuanbin Wang | Xun Xu | Yuqian Lu | Xun Xu | Huiyue Huang | Yuanbin Wang | Lei Yang
[1] Lifeng Xi,et al. Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods , 2007 .
[2] Fei Tao,et al. New IT Driven Service-Oriented Smart Manufacturing: Framework and Characteristics , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.
[3] Dimitris Mourtzis,et al. A Cloud-based Approach for Maintenance of Machine Tools and Equipment Based on Shop-floor Monitoring☆ , 2016 .
[4] Juan M. Corchado,et al. A review of edge computing reference architectures and a new global edge proposal , 2019, Future Gener. Comput. Syst..
[5] R. Isermann,et al. Model based detection of tool wear and breakage for machine tools , 1993, Proceedings of IEEE Systems Man and Cybernetics Conference - SMC.
[6] Juan M. Corchado,et al. Cloud Computing and Multiagent Systems, a Promising Relationship , 2016 .
[7] Wenyou Du,et al. Process Fault Detection Using Directional Kernel Partial Least Squares , 2015 .
[8] Ali Balador,et al. Communication middleware technologies for industrial distributed control systems: A literature review , 2017, 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA).
[9] Alois Knoll,et al. A Reference Architecture Based on Edge and Cloud Computing for Smart Manufacturing , 2019, 2019 28th International Conference on Computer Communication and Networks (ICCCN).
[10] Silvio Simani,et al. Reliability improvement of wind turbine power generation using model-based fault detection and fault tolerant control: A review , 2019, Renewable Energy.
[11] Krishna R. Pattipati,et al. Incremental Classifiers for Data-Driven Fault Diagnosis Applied to Automotive Systems , 2015, IEEE Access.
[12] Xin Zhou,et al. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data , 2016 .
[13] Huihui Yu,et al. A real time expert system for anomaly detection of aerators based on computer vision and surveillance cameras , 2020, J. Vis. Commun. Image Represent..
[14] Xun Xu,et al. A Cyber-Physical Machine Tools Platform using OPC UA and MTConnect , 2019, Journal of Manufacturing Systems.
[15] Rafiq Ahmad,et al. Intelligent assisted maintenance plan generation for corrective maintenance , 2019, Manufacturing Letters.
[16] Yan Xu,et al. A Digital-Twin-Assisted Fault Diagnosis Using Deep Transfer Learning , 2019, IEEE Access.
[17] Richard D. Braatz,et al. Fault Detection and Diagnosis in Industrial Systems , 2001 .
[18] Mohsen Guizani,et al. Edge Computing in the Industrial Internet of Things Environment: Software-Defined-Networks-Based Edge-Cloud Interplay , 2018, IEEE Communications Magazine.
[19] Andrew Y. C. Nee,et al. Digital Twin and Cloud, Fog, Edge Computing , 2019 .
[20] Yingfeng Zhang,et al. Real-time information capturing and integration framework of the internet of manufacturing things , 2015, Int. J. Comput. Integr. Manuf..
[21] Robert X. Gao,et al. Digital Twin for rotating machinery fault diagnosis in smart manufacturing , 2018, Int. J. Prod. Res..
[22] José Manuel Benítez,et al. Fault detection based on time series modeling and multivariate statistical process control , 2018, Chemometrics and Intelligent Laboratory Systems.
[23] Hwasoo Yeo,et al. A comparative study of time-based maintenance and condition-based maintenance for optimal choice of maintenance policy , 2016 .
[24] Suchitra Venkatesan,et al. Health monitoring and prognosis of electric vehicle motor using intelligent‐digital twin , 2019, IET Electric Power Applications.
[25] Hui Xiong,et al. Enhancing data analysis with noise removal , 2006, IEEE Transactions on Knowledge and Data Engineering.
[26] K. I. Ramachandran,et al. Fault diagnosis of spur bevel gear box using artificial neural network (ANN), and proximal support vector machine (PSVM) , 2010, Appl. Soft Comput..
[27] Guoqiang Hu,et al. Building Efficiency and Sustainability in the Tropics (SinBerBEST) , 2018 .
[28] Chih-Min Fan,et al. A Bayesian framework to integrate knowledge-based and data-driven inference tools for reliable yield diagnoses , 2008, 2008 Winter Simulation Conference.
[29] Robert X. Gao,et al. Cloud-enabled prognosis for manufacturing , 2015 .
[30] Meir Kalech,et al. Online data-driven anomaly detection in autonomous robots , 2014, Knowledge and Information Systems.
[31] Yuan Li,et al. Fault Detection Strategy Based on Weighted Distance of $k$ Nearest Neighbors for Semiconductor Manufacturing Processes , 2019, IEEE Transactions on Semiconductor Manufacturing.
[32] Pooja Kamat,et al. Anomaly Detection for Predictive Maintenance in Industry 4.0- A survey , 2020, E3S Web of Conferences.
[33] Xi Vincent Wang,et al. Condition Monitoring for Predictive Maintenance , 2018 .
[34] Andrew Y. C. Nee,et al. Enabling technologies and tools for digital twin , 2019 .
[35] Magnus Löfstrand,et al. Comparing a knowledge-based and a data-driven method in querying data streams for system fault detection: A hydraulic drive system application , 2014, Comput. Ind..
[36] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[37] Roberto Nardone,et al. Securing MQTT by Blockchain-Based OTP Authentication , 2020, Sensors.
[38] Didier Maquin,et al. Fault Detection and Isolation with Robust Principal Component Analysis , 2008, 2008 16th Mediterranean Conference on Control and Automation.
[39] Dimitris Mourtzis,et al. A cloud-based cyber-physical system for adaptive shop-floor scheduling and condition-based maintenance , 2018 .
[40] Kevin I-Kai Wang,et al. Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues , 2020, Robotics Comput. Integr. Manuf..
[41] Kamran Javed,et al. Robust, reliable and applicable tool wear monitoring and prognostic: Approach based on an improved-extreme learning machine , 2012, 2012 IEEE Conference on Prognostics and Health Management.
[42] Burkhard Hoppenstedt,et al. Anomaly Detections for Manufacturing Systems Based on Sensor Data—Insights into Two Challenging Real-World Production Settings , 2019, Sensors.
[43] Xavier Olive,et al. Recent Advances in Anomaly Detection Methods Applied to Aviation , 2019 .
[44] Balakrishnan Ramadoss,et al. Big data predictive analtyics for proactive semiconductor equipment maintenance , 2014, 2014 IEEE International Conference on Big Data (Big Data).
[45] Shahrul Kamaruddin,et al. A review of condition-based maintenance decision-making , 2012 .
[46] Jian Zhang,et al. How to model and implement connections between physical and virtual models for digital twin application , 2020 .
[47] Fabian Greif,et al. A Flexible Distributed Simulation Environment for Cyber-Physical Systems Using ZeroMQ , 2018, J. Commun..
[48] Yisha Xiang,et al. A review on condition-based maintenance optimization models for stochastically deteriorating system , 2017, Reliab. Eng. Syst. Saf..
[49] Chao Liu,et al. Web-based digital twin modeling and remote control of cyber-physical production systems , 2020, Robotics Comput. Integr. Manuf..
[50] Andreas Schwung,et al. Fault Detection Assessment using an extended FMEA and a Rule-based Expert System , 2019, 2019 IEEE 17th International Conference on Industrial Informatics (INDIN).
[51] Michel José Anzanello,et al. Fault detection in batch processes through variable selection integrated to multiway principal component analysis , 2019 .