Intelligent Maintenance Systems and Predictive Manufacturing
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Jay Lee | Jun Ni | Jaskaran Singh | Moslem Azamfar | Jianshe Feng | Baoyang Jiang | J. Lee | J. Ni | Baoyang Jiang | Jianshe Feng | M. Azamfar | Jaskaran Singh
[1] George J. Vachtsevanos,et al. Fault diagnosis and failure prognosis for engineering systems: A global perspective , 2009, 2009 IEEE International Conference on Automation Science and Engineering.
[2] Satinder Singh,et al. Bearing remaining useful life estimation using an adaptive data-driven model based on health state change point identification and K-means clustering , 2020, Measurement Science and Technology.
[3] H. W. Ngan,et al. Detection of Motor Bearing Outer Raceway Defect by Wavelet Packet Transformed Motor Current Signature Analysis , 2010, IEEE Transactions on Instrumentation and Measurement.
[4] Dirk Timmermann,et al. Survey on real-time communication via ethernet in industrial automation environments , 2014, Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA).
[5] Jay Lee,et al. Industrial Artificial Intelligence for industry 4.0-based manufacturing systems , 2018, Manufacturing Letters.
[6] Arnold H. Buss,et al. SHIFTING PRODUCTION BOTTLENECKS: CAUSES, CURES, AND CONUNDRUMS , 2009 .
[7] Bin Huang,et al. Review of PHM Data Competitions from 2008 to 2017 , 2018, Annual Conference of the PHM Society.
[8] Jay Lee,et al. A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems , 2015 .
[9] Kurt Matyas,et al. A procedural approach for realizing prescriptive maintenance planning in manufacturing industries , 2017 .
[10] Carlos Eduardo Pereira,et al. Manufacturing plant control: Challenges and issues , 2007 .
[11] Donghua Zhou,et al. Remaining useful life estimation - A review on the statistical data driven approaches , 2011, Eur. J. Oper. Res..
[12] Daniel N. Wilke,et al. Deep digital twins for detection, diagnostics and prognostics , 2020 .
[13] Jian Liu,et al. Quality-assured setup planning based on the stream-of-variation model for multi-stage machining processes , 2009 .
[14] Lihui Wang,et al. Current status and advancement of cyber-physical systems in manufacturing , 2015 .
[15] Jamie B. Coble,et al. A Review of Prognostics and Health Management Applications in Nuclear Power Plants , 2020, International Journal of Prognostics and Health Management.
[16] Gonzalo Mateos,et al. Health Monitoring and Management Using Internet-of-Things (IoT) Sensing with Cloud-Based Processing: Opportunities and Challenges , 2015, 2015 IEEE International Conference on Services Computing.
[17] Kenneth Reifsnider,et al. Multiphysics Stimulated Simulation Digital Twin Methods for Fleet Management , 2013 .
[18] Wenyu Zhao. An Integrated Framework of Performance Assessment and Drivetrain Prognostics for Wind Turbines , 2014 .
[19] Jay Lee,et al. Recent advances and trends in predictive manufacturing systems in big data environment , 2013 .
[20] Viktor Mayer-Schnberger,et al. Big Data: A Revolution That Will Transform How We Live, Work, and Think , 2013 .
[21] Salekul Islam,et al. Network Edge Intelligence for the Emerging Next-Generation Internet , 2010, Future Internet.
[22] Yong Chen,et al. Diagnosability Study of Multistage Manufacturing Processes Based on Linear Mixed-Effects Models , 2003, Technometrics.
[23] Jong-Myon Kim,et al. Acoustic Emission Sensor Network Based Fault Diagnosis of Induction Motors Using a Gabor Filter and Multiclass Support Vector Machines , 2016, Ad Hoc Sens. Wirel. Networks.
[24] Michael Schluse,et al. From simulation to experimentable digital twins: Simulation-based development and operation of complex technical systems , 2016, 2016 IEEE International Symposium on Systems Engineering (ISSE).
[25] Michael J. Roemer,et al. Online Ringing Characterization as a Diagnostic Technique for IGBTs in Power Drives , 2009, IEEE Transactions on Instrumentation and Measurement.
[26] Jay Lee. Smart Factory Systems , 2015, Informatik-Spektrum.
[27] Soma Bandyopadhyay,et al. Role Of Middleware For Internet Of Things: A Study , 2011 .
[28] Xun Xu,et al. From cloud computing to cloud manufacturing , 2012 .
[29] Sateesh Addepalli,et al. Fog computing and its role in the internet of things , 2012, MCC '12.
[30] Yongli Wei,et al. A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin , 2020, Robotics Comput. Integr. Manuf..
[31] Anis Chelbi,et al. Analysis of a production/inventory system with randomly failing production unit submitted to regular preventive maintenance , 2004, Eur. J. Oper. Res..
[32] Dazhong Wu,et al. A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing , 2017 .
[33] Lenz Belzner,et al. A Simulation-Based Architecture for Smart Cyber-Physical Systems , 2016, 2016 IEEE International Conference on Autonomic Computing (ICAC).
[34] Jay Lee,et al. Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications , 2014 .
[35] Richard Bateman,et al. e-Manufacturing: Characteristics, applications and potentials , 2008 .
[36] Dacfey Dzung,et al. Integration of a Wireless I/O Interface for PROFIBUS and PROFINET for Factory Automation , 2009, IEEE Transactions on Industrial Electronics.
[37] Min-Chun Pan,et al. Using appropriate IMFs for envelope analysis in multiple fault diagnosis of ball bearings , 2013 .
[38] N. Tandon,et al. A comparison of some condition monitoring techniques for the detection of defect in induction motor ball bearings , 2007 .
[39] Sang Do Noh,et al. Implementation of Cyber-Physical Production Systems for Quality Prediction and Operation Control in Metal Casting , 2018, Sensors.
[40] Ki Ling Cheung,et al. Joint determination of preventive maintenance and safety stocks in an unreliable production environment , 1997 .
[41] Daniel W. Apley,et al. Tolerance allocation for compliant beam structure assemblies , 2003 .
[42] Xiaolei Dong,et al. Security and Privacy for Cloud-Based IoT: Challenges , 2017, IEEE Communications Magazine.
[43] Jong-Myon Kim,et al. Discriminant Feature Distribution Analysis-Based Hybrid Feature Selection for Online Bearing Fault Diagnosis in Induction Motors , 2016, J. Sensors.
[44] Sandro Wartzack,et al. Shaping the digital twin for design and production engineering , 2017 .
[45] Jay Lee. Industrial AI: Applications with Sustainable Performance , 2020 .
[46] Karsten Schneider. Intelligent field devices in factory automation - modular structures into manufacturing cells , 2003, EFTA 2003. 2003 IEEE Conference on Emerging Technologies and Factory Automation. Proceedings (Cat. No.03TH8696).
[47] Michael Devetsikiotis,et al. Blockchains and Smart Contracts for the Internet of Things , 2016, IEEE Access.
[48] Robert X. Gao,et al. Deep learning and its applications to machine health monitoring , 2019, Mechanical Systems and Signal Processing.
[49] Jose Barata,et al. Big Data Analysis in Smart Manufacturing: A Review , 2017 .
[50] Jian Liu,et al. Predictive Control Considering Model Uncertainty for Variation Reduction in Multistage Assembly Processes , 2010, IEEE Transactions on Automation Science and Engineering.
[51] H.E.G. Powrie,et al. Engine health monitoring: Towards total prognostics , 1999, 1999 IEEE Aerospace Conference. Proceedings (Cat. No.99TH8403).
[52] George Chryssolouris,et al. The use of Digital Twin for predictive maintenance in manufacturing , 2019, Int. J. Comput. Integr. Manuf..
[53] Mengyan Nie,et al. Review of condition monitoring and fault diagnosis technologies for wind turbine gearbox , 2013 .
[54] Ivan Stojmenovic,et al. The Fog computing paradigm: Scenarios and security issues , 2014, 2014 Federated Conference on Computer Science and Information Systems.
[55] K. Goebel,et al. Prognostics approach for power MOSFET under thermal-stress aging , 2012, 2012 Proceedings Annual Reliability and Maintainability Symposium.
[56] Jay Lee,et al. A blockchain enabled Cyber-Physical System architecture for Industry 4.0 manufacturing systems , 2019, Manufacturing Letters.
[57] Philip Moore,et al. Cloud manufacturing – a critical review of recent development and future trends , 2017, Int. J. Comput. Integr. Manuf..
[58] Dragan Djurdjanovic,et al. Joint allocation of measurement points and controllable tooling machines in multistage manufacturing processes , 2010 .
[59] J. L. Dorrity,et al. Automated Feature Selection for Embeddable Prognostic and Health Monitoring (PHM) Architectures , 2006, 2006 IEEE Autotestcon.
[60] Stephan Biller,et al. Maintenance opportunity planning system , 2007 .
[61] F BabiceanuRadu,et al. Big Data and virtualization for manufacturing cyber-physical systems , 2016 .
[62] P.W. Kalgren,et al. Defining PHM, A Lexical Evolution of Maintenance and Logistics , 2006, 2006 IEEE Autotestcon.
[63] Jaskaran Singh,et al. Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis , 2020 .
[64] Yu Ding,et al. Process-oriented tolerancing for multi-station assembly systems , 2005 .
[65] Kevin Ashton,et al. That ‘Internet of Things’ Thing , 1999 .
[66] Stephan Biller,et al. The Costs of Downtime Incidents in Serial Multistage Manufacturing Systems , 2012 .
[67] Mandyam M. Srinivasan,et al. Production-Inventory Systems with Preventive Maintenance , 1996 .
[68] Fei Tao,et al. Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison , 2018, IEEE Access.
[69] Mahmut Parlar,et al. A survey of maintenance models for multi-unit systems , 1991 .
[70] Remzi Seker,et al. Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook , 2016, Comput. Ind..
[71] Duc Truong Pham,et al. A Reconfigurable Modeling Approach for Digital Twin-based Manufacturing System , 2019, Procedia CIRP.
[72] Fei Tao,et al. Digital twin-driven product design, manufacturing and service with big data , 2017, The International Journal of Advanced Manufacturing Technology.
[73] Jay Lee. E-manufacturing—fundamental, tools, and transformation , 2003 .
[74] Keith Jackson,et al. Digital Manufacturing and Flexible Assembly Technologies for Reconfigurable Aerospace Production Systems , 2016 .
[75] Averill M. Law,et al. Simulation Modeling and Analysis , 1982 .
[76] Paulo Leitão,et al. Industrial automation based on cyber-physical systems technologies: Prototype implementations and challenges , 2016, Comput. Ind..
[77] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[78] Leilani Battle,et al. Building the Internet of Things Using RFID: The RFID Ecosystem Experience , 2009, IEEE Internet Computing.
[79] Antonio Iera,et al. The Internet of Things: A survey , 2010, Comput. Networks.
[80] Zdenka Králová,et al. A SIMULATION APPROACH TO PRODUCTION LINE BOTTLENECK ANALYSIS , 2010 .
[81] Yoram Koren,et al. Stream-of-Variation Theory for Automotive Body Assembly , 1997 .
[82] Babak Nahid-Mobarakeh,et al. A Comprehensive Study on Shaft Voltages and Bearing Currents in Rotating Machines , 2018, IEEE Transactions on Industry Applications.
[83] Josef Noll,et al. Interoperability of Security-Enabled Internet of Things , 2011, Wirel. Pers. Commun..
[84] Martin White,et al. Internet of Things, Blockchain and Shared Economy Applications , 2016, EUSPN/ICTH.
[85] Lin Li,et al. Data driven bottleneck detection of manufacturing systems , 2009 .
[86] Manuel Díaz,et al. On blockchain and its integration with IoT. Challenges and opportunities , 2018, Future Gener. Comput. Syst..
[87] Krešimir Grgić,et al. A web-based IoT solution for monitoring data using MQTT protocol , 2016, 2016 International Conference on Smart Systems and Technologies (SST).
[88] Kang Chen,et al. Cloud Computing: System Instances and Current Research: Cloud Computing: System Instances and Current Research , 2010 .
[89] Huanyi Shui,et al. A Two-Layer Long Short-Term Memory Network for Bottleneck Prediction in Multi-Job Manufacturing Systems , 2018, Volume 3: Manufacturing Equipment and Systems.
[90] Edward H. Glaessgen,et al. The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles , 2012 .
[91] L. Jiang,et al. Fault Diagnosis of Rotating Machinery Based on Multisensor Information Fusion Using SVM and Time-Domain Features , 2014 .
[92] C. L. Philip Chen,et al. Data-intensive applications, challenges, techniques and technologies: A survey on Big Data , 2014, Inf. Sci..
[93] Birgit Vogel-Heuser,et al. Design, modelling, simulation and integration of cyber physical systems: Methods and applications , 2016, Comput. Ind..
[94] Jay Lee,et al. A systematic review of machine learning algorithms for prognostics and health management of rolling element bearings: fundamentals, concepts and applications , 2020, Measurement Science and Technology.
[95] Huanyi Shui,et al. Twofold Variation Propagation Modeling and Analysis for Roll-to-Roll Manufacturing Systems , 2019, IEEE Transactions on Automation Science and Engineering.
[96] László Monostori,et al. ScienceDirect Variety Management in Manufacturing . Proceedings of the 47 th CIRP Conference on Manufacturing Systems Cyber-physical production systems : Roots , expectations and R & D challenges , 2014 .
[97] O. M. Akanle,et al. An agent-based approach for e-manufacturing and supply chain integration , 2006, Comput. Ind. Eng..
[98] Martin Rostan,et al. EtherCAT enabled advanced control architecture , 2010, 2010 IEEE/SEMI Advanced Semiconductor Manufacturing Conference (ASMC).
[99] Marco Macchi,et al. A Digital Twin-based scheduling framework including Equipment Health Index and Genetic Algorithms , 2019, IFAC-PapersOnLine.
[100] Andrew Kusiak,et al. Data-driven smart manufacturing , 2018, Journal of Manufacturing Systems.
[101] Seungchul Lee,et al. Fault detection and identification method using observer-based residuals , 2018, Reliab. Eng. Syst. Saf..
[102] Yu Ding,et al. Optimal sensor distribution for variation diagnosis in multistation assembly processes , 2003, IEEE Trans. Robotics Autom..
[103] Athanasios V. Vasilakos,et al. A Manufacturing Big Data Solution for Active Preventive Maintenance , 2017, IEEE Transactions on Industrial Informatics.
[104] Yingfeng Zhang,et al. Real-time information capturing and integration framework of the internet of manufacturing things , 2015, Int. J. Comput. Integr. Manuf..
[105] Jiangtao Wen,et al. The IoT electric business model: Using blockchain technology for the internet of things , 2016, Peer-to-Peer Networking and Applications.
[106] Enrico Zio,et al. Artificial intelligence for fault diagnosis of rotating machinery: A review , 2018, Mechanical Systems and Signal Processing.
[107] Satinder Singh,et al. Bearing damage assessment using Jensen-Rényi Divergence based on EEMD , 2017 .
[108] Zheng Wei,et al. Cloud Computing:System Instances and Current Research , 2009 .
[109] Jianjun Shi,et al. State Space Modeling of Variation Propagation in Multistation Machining Processes Considering Machining-Induced Variations , 2012 .
[110] Ralph Deters,et al. Hosting Virtual IoT Resources on Edge-Hosts with Blockchain , 2016, 2016 IEEE International Conference on Computer and Information Technology (CIT).
[111] Gunnar Prytz,et al. A performance analysis of EtherCAT and PROFINET IRT , 2008, 2008 IEEE International Conference on Emerging Technologies and Factory Automation.
[112] In Lee,et al. The Internet of Things (IoT): Applications, investments, and challenges for enterprises , 2015 .
[113] Michael G. Pecht,et al. IoT-Based Prognostics and Systems Health Management for Industrial Applications , 2016, IEEE Access.
[114] Jay Lee,et al. Isolation-based feature Selection for Unsupervised Outlier Detection , 2019 .
[115] M. Pecht,et al. Review of offshore wind turbine failures and fault prognostic methods , 2012, Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing).
[116] J.W. Sheppard,et al. IEEE Standards for Prognostics and Health Management , 2008, IEEE Aerospace and Electronic Systems Magazine.
[117] R. Duggirala,et al. Predictive Monitoring and Control of the Cold Extrusion Process , 2000 .
[118] Michael J. Franklin,et al. Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing , 2012, NSDI.
[119] E. G. Kyriakidis,et al. Optimal preventive maintenance of a production system with an intermediate buffer , 2006, Eur. J. Oper. Res..
[120] Seokjun Lee,et al. Design and implementation of cybersecurity testbed for industrial IoT systems , 2017, The Journal of Supercomputing.
[121] Satinder Singh,et al. Rolling element bearing fault diagnosis based on Over-Complete rational dilation wavelet transform and auto-correlation of analytic energy operator , 2018 .
[122] George Q. Huang,et al. IoT-based real-time production logistics synchronization system under smart cloud manufacturing , 2016 .
[123] Jaime A. Camelio,et al. Modeling Variation Propagation of Multi-Station Assembly Systems With Compliant Parts , 2003 .
[124] Luca Podofillini,et al. Condition-based maintenance optimization by means of genetic algorithms and Monte Carlo simulation , 2002, Reliab. Eng. Syst. Saf..
[125] T. Chitra. Life based maintenance policy for minimum cost , 2003, Annual Reliability and Maintainability Symposium, 2003..
[126] Ana Beatriz Lopes de Sousa Jabbour,et al. Unlocking the circular economy through new business models based on large-scale data: An integrative framework and research agenda , 2017, Technological Forecasting and Social Change.
[127] B. McNaughton,et al. Hippocampal synaptic enhancement and information storage within a distributed memory system , 1987, Trends in Neurosciences.
[128] Enzo Baccarelli,et al. Fog of Everything: Energy-Efficient Networked Computing Architectures, Research Challenges, and a Case Study , 2017, IEEE Access.
[129] Jay Lee,et al. Deep learning-based cross-domain adaptation for gearbox fault diagnosis under variable speed conditions , 2020, Measurement Science and Technology.
[130] Ray Y. Zhong,et al. Intelligent Manufacturing in the Context of Industry 4.0: A Review , 2017 .
[131] Yingfeng Zhang,et al. A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products , 2017 .
[132] Jay Lee,et al. Convolutional Neural Network Based Rolling-Element Bearing Fault Diagnosis for Naturally Occurring and Progressing Defects Using Time-Frequency Domain Features , 2019, 2019 Prognostics and System Health Management Conference (PHM-Paris).
[133] Vidosav D. Majstorović,et al. Multistage manufacturing process control robust to inaccurate knowledge about process noise , 2017 .
[134] Sanjay H Upadhyay,et al. A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings , 2016 .
[135] Hu,et al. Dynamic Prediction Method of Production Logistics Bottleneck Based on Bottleneck Index , 2009 .
[136] Joaquín B. Ordieres Meré,et al. Smart factories in Industry 4.0: A review of the concept and of energy management approached in production based on the Internet of Things paradigm , 2014, 2014 IEEE International Conference on Industrial Engineering and Engineering Management.
[137] Frédéric Cugnon,et al. Evaluation of Machine Tool Digital Twin for machining operations in industrial environment , 2019, Procedia CIRP.
[138] Diego Cabrera,et al. A review on data-driven fault severity assessment in rolling bearings , 2018 .
[139] Hongzhou Wang,et al. A survey of maintenance policies of deteriorating systems , 2002, Eur. J. Oper. Res..
[140] Lei Zhang,et al. Dimensional errors of rollers in the stream of variation modeling in cold roll forming process of quadrate steel tube , 2008 .
[141] Masahiko Mori,et al. Machine monitoring system based on MTConnect technology , 2014 .
[142] Leandros Maglaras,et al. Security and Privacy in Fog Computing: Challenges , 2017, IEEE Access.
[143] Hongyu Li,et al. Study on planetary gear fault diagnosis based on entropy feature fusion of ensemble empirical mode decomposition , 2016 .
[144] J. Alberto Espinosa,et al. Big Data: Issues and Challenges Moving Forward , 2013, 2013 46th Hawaii International Conference on System Sciences.
[145] Jay Lee,et al. Introduction to e-Manufacturing , 2002, The Industrial Information Technology Handbook.
[146] Xi He,et al. Cloud Computing: a Perspective Study , 2010, New Generation Computing.
[147] Oscar Novo,et al. Blockchain Meets IoT: An Architecture for Scalable Access Management in IoT , 2018, IEEE Internet of Things Journal.
[148] Lin Li,et al. Throughput Bottleneck Prediction of Manufacturing Systems Using Time Series Analysis , 2011 .
[149] Sandhya Aneja,et al. Internet of Things: Vision, application areas and research challenges , 2017, 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC).
[150] Avita Katal,et al. Big data: Issues, challenges, tools and Good practices , 2013, 2013 Sixth International Conference on Contemporary Computing (IC3).
[151] Yongji Wang,et al. bottleneck prediction method based on improved adaptive network-based fuzzy inference system (anfis) in semiconductor manufacturing system , 2012 .
[152] Hamid Reza Karimi,et al. A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management , 2016 .
[153] Arwa Alrawais,et al. Fog Computing for the Internet of Things: Security and Privacy Issues , 2017, IEEE Internet Computing.
[154] Weiming Shen,et al. Computer supported collaborative design: Retrospective and perspective , 2008, Comput. Ind..
[155] P. Lall,et al. Prognostics Health Monitoring (PHM) for Prior-Damage Assessment in Electronics Equipment under Thermo-Mechanical Loads , 2007, 2007 Proceedings 57th Electronic Components and Technology Conference.
[156] Seungchul Lee,et al. Hidden maintenance opportunities in discrete and complex production lines , 2013, Expert Syst. Appl..
[157] Gunther Reinhart,et al. Integrated Production and Maintenance Planning for Cyber-physical Production Systems , 2018 .
[158] Jay Lee,et al. Intelligent prognostics tools and e-maintenance , 2006, Comput. Ind..
[159] K. Musselman,et al. The role of simulation in advanced planning and scheduling , 2002, Proceedings of the Winter Simulation Conference.
[160] Shichang Du,et al. Product lifecycle-oriented quality and productivity improvement based on stream of variation methodology , 2008, Comput. Ind..
[161] Imtiaz Ahmad,et al. Cloud Computing Pricing Models: A Survey , 2013 .
[162] Jay Lee,et al. Wind turbine performance degradation assessment based on a novel similarity metric for machine performance curves , 2016 .
[163] Jay Lee,et al. Industrial Big Data Analytics and Cyber-physical Systems for Future Maintenance & Service Innovation , 2015 .
[164] B. L. Ferrell. Air vehicle prognostics and health management , 2000, 2000 IEEE Aerospace Conference. Proceedings (Cat. No.00TH8484).
[165] Jie Chen,et al. Performance degradation assessment of a wind turbine gearbox based on multi-sensor data fusion , 2019, Mechanism and Machine Theory.
[166] Gerhard P. Hancke,et al. Industrial Wireless Sensor Networks: Challenges, Design Principles, and Technical Approaches , 2009, IEEE Transactions on Industrial Electronics.
[167] Lin Li,et al. Option model for joint production and preventive maintenance system , 2009 .
[168] Nir Kshetri,et al. Can Blockchain Strengthen the Internet of Things? , 2017, IT Professional.
[169] Bilal Ahmad,et al. Engineering Methods and Tools for Cyber–Physical Automation Systems , 2016, Proceedings of the IEEE.
[170] Qun Li,et al. A Survey of Fog Computing: Concepts, Applications and Issues , 2015, Mobidata@MobiHoc.
[171] He Zhang,et al. Digital Twin in Industry: State-of-the-Art , 2019, IEEE Transactions on Industrial Informatics.
[172] Yaguo Lei,et al. Machinery health prognostics: A systematic review from data acquisition to RUL prediction , 2018 .
[173] Jay Lee,et al. Cyber-physical Systems Architecture for Self-Aware Machines in Industry 4.0 Environment , 2015 .
[174] PRADIP KUMAR SHARMA,et al. A Software Defined Fog Node Based Distributed Blockchain Cloud Architecture for IoT , 2018, IEEE Access.
[175] Wensheng Xu,et al. RFID and ZigBee based manufacturing monitoring system , 2011, 2011 International Conference on Electric Information and Control Engineering.
[176] Yask Patel,et al. ZIGBEE: A LOW POWER WIRELESS TECHNOLOGY FOR INDUSTRIAL APPLICATIONS , 2012 .
[177] Hiroshi Motoda,et al. Feature Extraction, Construction and Selection , 1998 .
[178] P. Lall,et al. Prognostics Health Monitoring (PHM) for Prior Damage Assessment in Electronics Equipment Under Thermo-Mechanical Loads , 2011, IEEE Transactions on Components, Packaging and Manufacturing Technology.
[179] Jian Liu,et al. Process-oriented tolerancing using the extended stream of variation model , 2013, Comput. Ind..
[180] Wei Qiao,et al. Gearbox fault diagnosis using vibration and current information fusion , 2016, 2016 IEEE Energy Conversion Congress and Exposition (ECCE).
[181] Yong Zhao,et al. Cloud Computing and Grid Computing 360-Degree Compared , 2008, GCE 2008.
[182] Lihui Wang,et al. Cloud-enhanced predictive maintenance , 2018 .
[183] Jiguo Yu,et al. A Privacy Preserving Communication Protocol for IoT Applications in Smart Homes , 2017, IEEE Internet of Things Journal.
[184] Hiroshi Motoda,et al. Feature Extraction, Construction and Selection: A Data Mining Perspective , 1998 .
[185] R. S. Gunerkar,et al. Classification of Ball Bearing Faults Using Vibro-Acoustic Sensor Data Fusion , 2019, Experimental Techniques.
[186] Fouad Slaoui-Hasnaoui,et al. Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges , 2014 .
[187] Laurent Massoulié,et al. Greening the internet with nano data centers , 2009, CoNEXT '09.
[188] Michael J. Roemer,et al. Improving digital system diagnostics through Prognostic and Health Management (PHM) technology , 2007, 2007 IEEE Autotestcon.
[189] Lee Garber,et al. Using In-Memory Analytics to Quickly Crunch Big Data , 2012, Computer.
[190] Qiang Huang,et al. Stream of Variation Modeling and Analysis of Serial-Parallel , 2004 .
[191] Kenneth Ward Church,et al. On Delivering Embarrassingly Distributed Cloud Services , 2008, HotNets.
[192] M. Shamim Hossain,et al. Cloud-assisted Industrial Internet of Things (IIoT) - Enabled framework for health monitoring , 2016, Comput. Networks.
[193] Theodoros Loutas,et al. The combined use of vibration, acoustic emission and oil debris on-line monitoring towards a more effective condition monitoring of rotating machinery , 2011 .
[194] Qiang Huang,et al. Diagnosis of multi-operational machining processes through variation propagation analysis , 2002 .
[195] Jiong Tang,et al. Wind Turbine Gearbox Fault Detection Using Multiple Sensors With Feature Level Data Fusion , 2011 .
[196] Hai Jin,et al. Towards Pay-As-You-Consume Cloud Computing , 2011, 2011 IEEE International Conference on Services Computing.
[197] Duc Truong Pham,et al. Integrated production machines and systems – beyond lean manufacturing , 2008 .
[198] Weihua Li,et al. Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network , 2017, IEEE Transactions on Instrumentation and Measurement.
[199] Noureddine Zerhouni,et al. Health assessment and life prediction of cutting tools based on support vector regression , 2015, J. Intell. Manuf..
[200] Yaguo Lei,et al. Condition monitoring and fault diagnosis of planetary gearboxes: A review , 2014 .
[201] Stephan Biller,et al. Transient Analysis of Downtimes and Bottleneck Dynamics in Serial Manufacturing Systems , 2010 .
[202] Andrew Y. C. Nee,et al. Digital twin driven prognostics and health management for complex equipment , 2018 .
[203] Fei Tao,et al. Cloud manufacturing: a computing and service-oriented manufacturing model , 2011 .
[204] Soundar R. T. Kumara,et al. Cyber-physical systems in manufacturing , 2016 .
[205] Wael M. Mohammed,et al. Connecting web-based IoT devices to a cloud-based manufacturing platform , 2017, IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society.
[206] Tangbin Xia,et al. Production-driven opportunistic maintenance for batch production based on MAM-APB scheduling , 2015, Eur. J. Oper. Res..
[207] Nilüfer Çenesiz,et al. Controller area network (CAN) for computer integrated manufacturing systems , 2004, J. Intell. Manuf..
[208] Qun Li,et al. Security and Privacy Issues of Fog Computing: A Survey , 2015, WASA.
[209] Mahmoud Omid,et al. Classifier fusion of vibration and acoustic signals for fault diagnosis and classification of planetary gears based on Dempster–Shafer evidence theory , 2014 .
[210] Fakhri Karray,et al. Multisensor data fusion: A review of the state-of-the-art , 2013, Inf. Fusion.
[211] J. S. Zuback,et al. Building blocks for a digital twin of additive manufacturing , 2017 .
[212] Jun Ni,et al. Online stochastic control of dimensional quality in multistation manufacturing systems , 2007 .
[213] Robert X. Gao,et al. A new paradigm of cloud-based predictive maintenance for intelligent manufacturing , 2015, Journal of Intelligent Manufacturing.
[214] Daming Lin,et al. A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .
[215] Chao Zhang,et al. Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing , 2019, Int. J. Prod. Res..
[216] David,et al. A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size , 2006 .
[217] Vidhya Balasubramanian,et al. A Comparative Study of Vision Based Human Detection Techniques in People Counting Applications , 2015 .
[218] Jay Lee,et al. Cyber physical systems for predictive production systems , 2017, Production Engineering.
[219] Jionghua Jin,et al. State Space Modeling of Sheet Metal Assembly for Dimensional Control , 1999 .
[220] K.K.B. Hon,et al. Performance and Evaluation of Manufacturing Systems , 2005 .
[221] Jay Lee,et al. Detection and diagnosis of bottle capping failures based on motor current signature analysis , 2019 .
[222] Bhabendu Kumar Mohanta,et al. An Overview of Smart Contract and Use Cases in Blockchain Technology , 2018, 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT).
[223] Lida Xu,et al. Internet of Things for Enterprise Systems of Modern Manufacturing , 2014, IEEE Transactions on Industrial Informatics.
[224] Andrea Tarallo,et al. A cyber-physical system for production monitoring of manual manufacturing processes , 2018, International Journal on Interactive Design and Manufacturing (IJIDeM).
[225] Ye Tao. Method of Simulation on Determining Bottleneck Resource , 2003 .
[226] George Q. Huang,et al. Toward open manufacturing: A cross-enterprises knowledge and services exchange framework based on blockchain and edge computing , 2017, Ind. Manag. Data Syst..
[227] Arturo Molina,et al. Enterprise integration and interoperability in manufacturing systems: Trends and issues , 2008, Comput. Ind..
[228] Semyon M. Meerkov,et al. Transient behavior of serial production lines with Bernoulli machines , 2008 .
[229] Raouf Boutaba,et al. Cloud computing: state-of-the-art and research challenges , 2010, Journal of Internet Services and Applications.
[230] Andreas Werner,et al. Approach for a Holistic Predictive Maintenance Strategy by Incorporating a Digital Twin , 2019, Procedia Manufacturing.
[231] Rolf Steinhilper,et al. The Digital Twin: Demonstrating the Potential of Real Time Data Acquisition in Production Systems ☆ , 2017 .
[232] David Wetherall,et al. Revisiting Smart Dust with RFID Sensor Networks , 2008, HotNets.
[233] Huanyi Shui,et al. Virtual sensing and virtual metrology for spatial error monitoring of roll-to-roll manufacturing systems , 2019, CIRP Annals.
[234] Jay Lee,et al. Integration of digital twin and deep learning in cyber‐physical systems: towards smart manufacturing , 2020 .
[235] Jun Ni,et al. Measurement Scheme Synthesis in Multi-Station Machining Systems , 2004 .
[236] Joseph Mathew,et al. Rotating machinery prognostics. State of the art, challenges and opportunities , 2009 .
[237] Max Felser,et al. Real-Time Ethernet - Industry Prospective , 2005, Proceedings of the IEEE.
[238] Khaled Salah,et al. IoT security: Review, blockchain solutions, and open challenges , 2017, Future Gener. Comput. Syst..
[239] Peng Wang,et al. An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox , 2017, Sensors.