Intelligent Maintenance Systems and Predictive Manufacturing

[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.