Vertical data continuity with lean edge analytics for industry 4.0 production

[1]  Eberhard Abele,et al.  Effiziente Fabrik 4.0 , 2015 .

[2]  Paulo Leitão,et al.  IDARTS - Towards intelligent data analysis and real-time supervision for industry 4.0 , 2018, Comput. Ind..

[3]  Stefan Rüping,et al.  Künstliche Intelligenz und die Potenziale des maschinellen Lernens für die Industrie , 2017, Wirtschaftsinformatik Manag..

[4]  Stefan Jablonski,et al.  Digital Connected Production: Wearable Manufacturing Information Systems , 2017, OTM Workshops.

[5]  Isabel Praça,et al.  An Architecture for Proactive Maintenance in the Machinery Industry , 2017, ISAmI.

[6]  Dazhong Wu,et al.  A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing , 2017 .

[7]  Zhiqiang Ge,et al.  Distributed predictive modeling framework for prediction and diagnosis of key performance index in plant-wide processes , 2017 .

[8]  J. Lee,et al.  Recent Advances and Trends of Cyber-Physical Systems and Big Data Analytics in Industrial Informatics , 2014 .

[9]  K. Wang,et al.  Intelligent Predictive Maintenance ( IPdM ) System – Industry 4.0 Scenario , 2016 .

[10]  Wilfried Sihn,et al.  Digital Twin in manufacturing: A categorical literature review and classification , 2018 .

[11]  Jing Li,et al.  Knowledge discovery from observational data for process control using causal Bayesian networks , 2007 .

[12]  Stefan Schönig,et al.  Lean data with edge analytics : Decentralized current profile analysis on embedded systems using neural networks , 2020 .

[13]  Noureddine Zerhouni,et al.  A new approach of PHM as a service in cloud computing , 2016, 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt).

[14]  A. Chan,et al.  Using Bayesian networks to improve fault diagnosis during manufacturing tests of mobile telephone infrastructure , 2008, J. Oper. Res. Soc..

[15]  Elisa Negri,et al.  Review of digital twin applications in manufacturing , 2019, Comput. Ind..

[16]  Jay Lee,et al.  Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment , 2014 .

[17]  Arnim Reger,et al.  A PLC-BASED MEASURING SYSTEM FOR MACHINE CROSSLINKING AND MONITORING , 2018 .

[18]  Bastian Ziegler,et al.  Lean Data in Manufacturing Systems: Using Artificial Intelligence for Decentralized Data Reduction and Information Extraction , 2018 .

[19]  Sudarsan Rachuri,et al.  Predictive Analytics Model for Power Consumption in Manufacturing , 2014 .

[20]  Manoj Kumar Tiwari,et al.  Data mining in manufacturing: a review based on the kind of knowledge , 2009, J. Intell. Manuf..

[21]  José Barata Oliveira,et al.  A Highly Flexible, Distributed Data Analysis Framework for Industry 4.0 Manufacturing Systems , 2016, SOHOMA.

[22]  Dominic T. J. O'Sullivan,et al.  A comparison of fog and cloud computing cyber-physical interfaces for Industry 4.0 real-time embedded machine learning engineering applications , 2019, Comput. Ind..

[23]  Anantha Narayanan,et al.  Towards a domain-specific framework for predictive analytics in manufacturing , 2014, 2014 IEEE International Conference on Big Data (Big Data).

[24]  Stefan Schönig,et al.  IoT meets BPM: a bidirectional communication architecture for IoT-aware process execution , 2020, Software and Systems Modeling.

[25]  A. Zabala,et al.  Advanced fault prediction in high-precision foundry production , 2008, 2008 6th IEEE International Conference on Industrial Informatics.

[26]  Axel Jantsch,et al.  SAMBA: A self-aware health monitoring architecture for distributed industrial systems , 2017, IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society.

[27]  Rolf Steinhilper,et al.  The Digital Twin: Realizing the Cyber-Physical Production System for Industry 4.0☆ , 2017 .