Cyber physical process monitoring systems

Manufacturing process monitoring systems is evolving from centralised bespoke applications to decentralised reconfigurable collectives. The resulting cyber-physical systems are made possible through the integration of high power computation, collaborative communication, and advanced analytics. This digital age of manufacturing is aimed at yielding the next generation of innovative intelligent machines. The focus of this research is to present the design and development of a cyber-physical process monitoring system; the components of which consist of an advanced signal processing chain for the semi-autonomous process characterisation of a CNC turning machine tool. The novelty of this decentralised system is its modularity, reconfigurability, openness, scalability, and unique functionality. The function of the decentralised system is to produce performance criteria via spindle vibration monitoring, which is correlated to the occurrence of sequential process events via motor current monitoring. Performance criteria enables the establishment of normal operating response of machining operations, and more importantly the identification of abnormalities or trends in the sensor data that can provide insight into the quality of the process ongoing. The function of each component in the signal processing chain is reviewed and investigated in an industrial case study.

[1]  John H. Lilly Fuzzy Control and Identification: Lilly/Fuzzy Control , 2010 .

[2]  C. A. van Luttervelt,et al.  Toward a resilient manufacturing system , 2011 .

[3]  I. Calvo,et al.  Distribution middleware technologies for Cyber Physical Systems , 2012, 2012 9th International Conference on Remote Engineering and Virtual Instrumentation (REV).

[4]  Krzysztof Jemielniak,et al.  Advanced monitoring of machining operations , 2010 .

[5]  Athulan Vijayaraghavan,et al.  Automated energy monitoring of machine tools , 2010 .

[6]  Svend Gade,et al.  Analyzers and Signal Generators , 2008 .

[7]  Alessandro Margara,et al.  Complex event processing with T-REX , 2012, J. Syst. Softw..

[8]  George I. Cohn,et al.  Electromagnetic induction , 2019, Science and Mathematics for Engineering.

[9]  P. Lindgren,et al.  Real-time complex event processing using concurrent reactive objects , 2013, 2013 IEEE International Conference on Industrial Technology (ICIT).

[10]  Qiang Liu,et al.  Research on data-sharing and intelligent CNC machining system , 2015, 2015 IEEE International Conference on Mechatronics and Automation (ICMA).

[11]  Qiang Wang,et al.  Intelligent assembly system for mechanical products and key technology based on internet of things , 2014, Journal of Intelligent Manufacturing.

[12]  Andrew Jarvis,et al.  Strategies for Minimum Energy Operation for Precision Machining , 2009 .

[13]  Reza Abrishambaf,et al.  Integration of Wireless Sensor Networks into the distributed intelligent manufacturing within the framework of IEC 61499 function blocks , 2011, 2011 IEEE International Conference on Systems, Man, and Cybernetics.

[14]  Stamatis Karnouskos,et al.  Dynamically Optimized Production Planning Using Cross-Layer SOA , 2008, 2008 32nd Annual IEEE International Computer Software and Applications Conference.

[15]  João Reis,et al.  Sensor cloud: SmartComponent framework for reconfigurable diagnostics in intelligent manufacturing environments , 2015, 2015 IEEE 13th International Conference on Industrial Informatics (INDIN).

[16]  J. Morgan,et al.  A service-oriented reconfigurable process monitoring system - enabling cyber physical systems , 2014 .

[17]  Pieter Abbeel,et al.  Image Object Label 3 D CAD Model Candidate Grasps Google Object Recognition Engine Google Cloud Storage Select Feasible Grasp with Highest Success Probability Pose EstimationCamera Robots Cloud 3 D Sensor , 2014 .

[18]  Huai Gao,et al.  A modeling method of task-oriented energy consumption for machining manufacturing system , 2012 .

[19]  John H. Lilly,et al.  Fuzzy Control and Identification , 2010 .

[20]  Jeff Morgan,et al.  Enabling a ubiquitous and cloud manufacturing foundation with field-level service-oriented architecture , 2017, Int. J. Comput. Integr. Manuf..

[21]  Xinghuo Yu,et al.  Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey , 2016, IEEE Transactions on Industrial Informatics.

[22]  Pawel Pietrzak,et al.  Towards a lightweight CEP engine for embedded systems , 2012, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society.

[23]  Andrei Lobov,et al.  OPC-UA and DPWS interoperability for factory floor monitoring using complex event processing , 2011, 2011 9th IEEE International Conference on Industrial Informatics.

[24]  Madan M. Gupta,et al.  An Innovative Fuzzy-Neural Decision Analyzer for Qualitative Group Decision Making , 2015, Int. J. Inf. Technol. Decis. Mak..

[25]  G. E. O'Donell,et al.  Data interoperability for reconfigurable manufacturing process monitoring systems , 2013 .

[26]  Christoph Herrmann,et al.  Glocalized Solutions for Sustainability in Manufacturing , 2011 .

[27]  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 .

[28]  H. B. Mitchell,et al.  Multi-Sensor Data Fusion: An Introduction , 2007 .

[29]  K.K.B. Hon,et al.  Performance and Evaluation of Manufacturing Systems , 2005 .

[30]  Robert Schmitt,et al.  Modelling Machine Tools for Self-Optimisation of Energy Consumption , 2011 .

[31]  T. N. Wong,et al.  Service-oriented architecture for ontologies supporting multi-agent system negotiations in virtual enterprise , 2012, J. Intell. Manuf..

[32]  Robert X. Gao,et al.  Cloud-enabled prognosis for manufacturing , 2015 .

[33]  Frank L. Lewis,et al.  Intelligent Fault Diagnosis and Prognosis for Engineering Systems , 2006 .

[34]  Francisco Restivo,et al.  Decision support system for Petri nets enabled automation components , 2009, 2009 7th IEEE International Conference on Industrial Informatics.

[35]  K. Walzer,et al.  Event-driven manufacturing: Unified management of primitive and complex events for manufacturing monitoring and control , 2008, 2008 IEEE International Workshop on Factory Communication Systems.

[36]  L. D. Paarmann,et al.  Design and Analysis of Analog Filters: A Signal Processing Perspective , 2001 .

[37]  Maria Leonilde Rocha Varela,et al.  Cloudlet architecture for dashboard in cloud and ubiquitous manufacturing , 2013 .

[38]  Vicent J. Botti,et al.  Holons and agents , 2004, J. Intell. Manuf..

[39]  Li Tan,et al.  Digital Signal Processing Systems, Basic Filtering Types, and Digital Filter Realizations , 2013 .

[40]  P. Leitao,et al.  Service-oriented SCADA and MES supporting Petri nets based orchestrated automation systems , 2012, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society.

[41]  Garret E. O’Donnell,et al.  Position-oriented process monitoring in freeform abrasive machining , 2013 .

[42]  Damien Trentesaux,et al.  Distributed control of production systems , 2009, Eng. Appl. Artif. Intell..

[43]  Fei Tao,et al.  IoT-Based Intelligent Perception and Access of Manufacturing Resource Toward Cloud Manufacturing , 2014, IEEE Transactions on Industrial Informatics.

[44]  Martin Eckstein,et al.  Monitoring of Drilling Process for Highly Stressed Aeroengine Components , 2012 .