A practical and synchronized data acquisition network architecture for industrial robot predictive maintenance in manufacturing assembly lines

[1]  Guixiu Qiao,et al.  Advancing Measurement Science to Assess Monitoring, Diagnostics, and Prognostics for Manufacturing Robotics. , 2016, International journal of prognostics and health management.

[2]  Rodrigo da Rosa Righi,et al.  Predictive maintenance in the Industry 4.0: A systematic literature review , 2020, Comput. Ind. Eng..

[3]  Sami Kara,et al.  Manufacturing big data ecosystem: A systematic literature review , 2020, Robotics Comput. Integr. Manuf..

[4]  Panagiotis Aivaliotis,et al.  Degradation curves integration in physics-based models: Towards the predictive maintenance of industrial robots , 2021, Robotics Comput. Integr. Manuf..

[5]  Lida Xu,et al.  Big data for cyber physical systems in industry 4.0: a survey , 2019, Enterp. Inf. Syst..

[6]  H. Van Brussel,et al.  Non-linear dynamics tools for the motion analysis and condition monitoring of robot joints , 2001 .

[7]  Luka Eciolaza,et al.  Towards manufacturing robotics accuracy degradation assessment: A vision-based data-driven implementation , 2021, Robotics Comput. Integr. Manuf..

[8]  B. Samanta,et al.  Prognostics of machine condition using soft computing , 2008 .

[9]  Mikael Norrlöf,et al.  A data-driven approach to diagnostics of repetitive processes in the distribution domain – Applications to gearbox diagnostics in industrial robots and rotating machines , 2014 .

[10]  Tao Liu,et al.  RESEARCH ON CONDITION MONITORING OF SPEED REDUCER OF INDUSTRIAL ROBOT WITH ACOUSTIC EMISSION , 2016 .

[11]  Thyago P. Carvalho,et al.  A systematic literature review of machine learning methods applied to predictive maintenance , 2019, Comput. Ind. Eng..

[12]  Sang Do Noh,et al.  Implementation of Cyber-Physical Production Systems for Quality Prediction and Operation Control in Metal Casting , 2018, Sensors.

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

[14]  Hongchao Wang,et al.  Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey , 2019, IEEE Systems Journal.

[15]  Costas J. Spanos,et al.  Diagnosing wind turbine faults using machine learning techniques applied to operational data , 2016, 2016 IEEE International Conference on Prognostics and Health Management (ICPHM).

[16]  Nazmus Sakib,et al.  Challenges and Opportunities of Condition-based Predictive Maintenance: A Review , 2018 .

[17]  Juan S. Toquica,et al.  Web Compliant Open Architecture For Teleoperation of Industrial Robots , 2019, 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE).

[18]  Xiaolong Xu,et al.  Big data challenges and opportunities in the hype of Industry 4.0 , 2017, 2017 IEEE International Conference on Communications (ICC).

[19]  André Carvalho Bittencourt,et al.  A Data-driven Method for Monitoring Systems that Operate Repetitively -Applications to Wear Monitoring in an Industrial Robot Joint1 , 2012 .

[20]  Urko Zurutuza,et al.  A Methodology and Experimental Implementation for Industrial Robot Health Assessment via Torque Signature Analysis , 2020 .

[21]  Weizhong Yan,et al.  One-class extreme learning machines for gas turbine combustor anomaly detection , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[22]  Yang Hu,et al.  Feature learning for fault detection in high-dimensional condition monitoring signals , 2018, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability.

[23]  Tawfik Borgi,et al.  Data analytics for predictive maintenance of industrial robots , 2017, 2017 International Conference on Advanced Systems and Electric Technologies (IC_ASET).

[24]  Gian Antonio Susto,et al.  A One-Class SVM Based Tool for Machine Learning Novelty Detection in HVAC Chiller Systems , 2014 .

[25]  Bo-Suk Yang,et al.  Intelligent prognostics for battery health monitoring based on sample entropy , 2011, Expert Syst. Appl..

[26]  David A. Clifton,et al.  A review of novelty detection , 2014, Signal Process..

[27]  Michael G. Pecht,et al.  A prognostics and health management roadmap for information and electronics-rich systems , 2010, Microelectron. Reliab..

[28]  Yongli Wei,et al.  A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin , 2020, Robotics Comput. Integr. Manuf..