A new approach of PHM as a service in cloud computing

Smart Manufacturing is the fourth revolution in the manufacturing industry “industry 4.0”. The integration of Internet of things and Cloud manufacturing becomes increasingly important for the development of the industry 4.0. The objective of industry 4.0 is not just to improve the production management but also manage and reduce equipment downtime, for that many strategies could be implemented to ensure the good condition of equipment. The efficient one is the predictive maintenance which collects information from sensors and predicts the malfunction or failure in the system. Prognostics Health Manager (PHM) offers significant benefits for maintenance. It predicts the future behavior of a system as well as its remaining useful life. This paper offers a new architecture to provide PHM solutions as a service in cloud computing environment (PHM-SaaS, PHM-PaaS, PHM-IaaS). We defined the entities and actors as well as their behavior in this architecture. To test our approach we were interested by the prognostics and post-prognostics process which are the main steps in the PHM technology, the remaining useful life is estimated in the prognostics step and according to this RUL a decision is automatically provided by the post prognostics process.

[1]  Soheyb Ayad,et al.  Towards a new cloud robotics approach , 2015, 2015 10th International Symposium on Mechatronics and its Applications (ISMA).

[2]  Jay Lee,et al.  A comprehensive framework of factory-to-factory dynamic fleet-level prognostics and operation management for geographically distributed assets , 2015, 2015 IEEE International Conference on Automation Science and Engineering (CASE).

[3]  Mathias Schmitt,et al.  Human-machine-interaction in the industry 4.0 era , 2014, 2014 12th IEEE International Conference on Industrial Informatics (INDIN).

[4]  N. Iyer,et al.  Framework for post-prognostic decision support , 2006, 2006 IEEE Aerospace Conference.

[5]  Noureddine Zerhouni,et al.  PEMFC aging modeling for prognostics and health assessment , 2015 .

[6]  Noureddine Zerhouni,et al.  Health assessment and life prediction of cutting tools based on support vector regression , 2015, J. Intell. Manuf..

[7]  Rainer Drath,et al.  Industrie 4.0: Hit or Hype? [Industry Forum] , 2014, IEEE Industrial Electronics Magazine.

[8]  Min Xia,et al.  Closed-loop design evolution of engineering system using condition monitoring through internet of things and cloud computing , 2016, Comput. Networks.

[9]  Jiafu Wan,et al.  Industrie 4.0: Enabling technologies , 2015, Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things.

[10]  Peter Friess,et al.  Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems , 2013 .

[11]  Noureddine Zerhouni,et al.  A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models , 2012, IEEE Transactions on Reliability.

[12]  Zahir Tari,et al.  SaaS clouds supporting non computing specialists , 2014, 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA).

[13]  Noureddine Zerhouni,et al.  Prognostics of PEM fuel cell in a particle filtering framework , 2014 .

[14]  Dazhong Wu,et al.  Cloud Manufacturing: Drivers, Current Status, and Future Trends , 2013 .