Deployment of Prognostics Technologies and Tools for Asset Management: Platforms and Applications

Over recent years, a significant amount of research has been dedicated to the development of Prognostics and Health Management (PHM) models. However, less attention is paid towards implementing the developed models to real-world applications to serve the needs of industry. In order to successfully implement a PHM system, a systematic approach is required to deploy the developed analytic tools (algorithms, software and agents) using a scalable hardware platform. In this paper, different PHM deployment platforms including stand-alone PC, embedded and cloud-based platforms are benchmarked. Then, a unified strategy for deploying the developed PHM tools using each of these platforms is presented. A smart deployment platform selection method using Quality Function Deployment (QFD) is also introduced. Following that, several case studies from different applications are provided as examples to demonstrate the capabilities and limitations of each deployment platform.

[1]  Robert X. Gao,et al.  Smart bearing utilizing embedded sensors: design considerations , 1997, Smart Structures.

[2]  Tommy W. S. Chow,et al.  Induction machine fault detection using SOM-based RBF neural networks , 2004, IEEE Transactions on Industrial Electronics.

[3]  E. Bechhoefer,et al.  Algorithms for embedded PHM , 2012, 2012 IEEE Conference on Prognostics and Health Management.

[4]  Philip A. Scarf,et al.  On the application of mathematical models in maintenance , 1997 .

[5]  Yaoyu Li,et al.  Computationally Efficient Data-Driven Surge Map Modeling for Centrifugal Air Compressors , 2007, 2007 American Control Conference.

[6]  Lin Ma,et al.  Prognostic modelling options for remaining useful life estimation by industry , 2011 .

[7]  Jay Lee,et al.  iFactory Cloud Service Platform Based on IMS Tools and Servo-lution , 2014 .

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

[9]  Manuel Rodríguez-Martínez,et al.  Open Source Cloud Computing Tools: A Case Study with a Weather Application , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[10]  Lin Li,et al.  Informatics platform for designing and deploying e-manufacturing systems , 2009 .

[11]  Hamid A. Toliyat,et al.  Condition monitoring and fault diagnosis of electrical machines-a review , 1999, Conference Record of the 1999 IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No.99CH36370).

[12]  Zheng Hu,et al.  A Technical Framework and Roadmap of Embedded Diagnostics and Prognostics for Complex Mechanical Systems in Prognostics and Health Management Systems , 2012, IEEE Transactions on Reliability.

[13]  Fulei Chu,et al.  Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography , 2004 .

[14]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[15]  Ruxu Du,et al.  Fault diagnosis using support vector machine with an application in sheet metal stamping operations , 2004 .

[16]  Jay Lee,et al.  Design of a reconfigurable prognostics platform for machine tools , 2010, Expert Syst. Appl..

[17]  P.G. McLaren,et al.  Reliability improvement and economic benefits of on-line monitoring systems for large induction machines , 1988, Conference Record of the 1988 IEEE Industry Applications Society Annual Meeting.

[18]  Weisong Shi,et al.  Wukong: A cloud-oriented file service for mobile Internet devices , 2012, J. Parallel Distributed Comput..

[19]  Satnam Singh,et al.  A prognostic framework for health management of coupled systems , 2011, 2011 IEEE Conference on Prognostics and Health Management.

[20]  Yongyong He,et al.  Hidden Markov model-based fault diagnostics method in speed-up and speed-down process for rotating machinery , 2005 .

[21]  Douglas C. Schmidt,et al.  Model-driven auto-scaling of green cloud computing infrastructure , 2012, Future Gener. Comput. Syst..

[22]  Jack B. Revelle,et al.  The QFD handbook , 1998 .

[23]  Byung-Gon Chun,et al.  Augmented Smartphone Applications Through Clone Cloud Execution , 2009, HotOS.

[24]  Joseph Mathew,et al.  Rotating machinery prognostics. State of the art, challenges and opportunities , 2009 .

[25]  Arnd Schirrmann,et al.  Making use of prognostics health management information for aerospace spare components logistics network optimisation , 2011, Comput. Ind..

[26]  Cpm Cor Govers,et al.  What and how about quality function deployment (QFD) , 1996 .

[27]  Raouf Boutaba,et al.  Cloud computing: state-of-the-art and research challenges , 2010, Journal of Internet Services and Applications.

[28]  Jay Lee.  Recent Advances and Trends on Cloud-based Machinery Prognostics and Health Management , 2012, PECCS.

[29]  Zhibin Zhou,et al.  Secure data processing framework for mobile cloud computing , 2011, 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[30]  Jinwu Xu,et al.  An Implementation of a Remote Diagnostic System on Rotational Machines , 2006 .

[31]  Johann Schumann,et al.  The Case for Software Health Management , 2011, 2011 IEEE Fourth International Conference on Space Mission Challenges for Information Technology.

[32]  P. Mell,et al.  What's Special about Cloud Security? , 2012, IT Professional.

[33]  Jay Lee,et al.  A review on prognostics and health monitoring of Li-ion battery , 2011 .