Towards the Industrial Application of PHM: Challenges and Methodological Approach

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Jul 8, 2014
Antonio J Guillén López Adolfo Crespo Márquez Juan Fco. Gómez Fernández Alejandro Guerrero Bolaños

Abstract

The diagnosis and prognosis capabilities are the key points of PHM (Prognosis Health Management) research. Most of the endeavor and investment are being oriented to get and improve these capabilities: new sensors, measurement techniques, communication/data solutions, detection algorithms, decision algorithms and reliability calculate tools. Nowadays it is actually possible take advantage of these capabilities to improve systems operation and maintenance. In spite of this, massive industrial application is still far away. Many of industrial sectors barely have heard about of PHM and its potential, or only have introduced classical CBM (Condition Based Maintenance) tools -vibration analysis, ultrasound, thermography- to specific and local maintenance applications.
In this paper a comprehensive understanding of the problem of transferring PHM into industrial environments and its relevance is introduced. It's also argued the need of develop a methodological approach as a key point for getting a broad applying of PHM-based solutions. To do this, the main challenges to be addressed are listed and analyzed..

How to Cite

López, A. J. G., Márquez, A. C., Fernández, J. F. G., & Bolaños, A. G. (2014). Towards the Industrial Application of PHM: Challenges and Methodological Approach. PHM Society European Conference, 2(1). https://doi.org/10.36001/phme.2014.v2i1.1563
Abstract 1662 | PDF Downloads 206

##plugins.themes.bootstrap3.article.details##

Keywords

PHM, PHM industrial applications, PHM methodologies

References
Barberá L., Crespo A., Viveros P., Arata A., (2012). The Graphical Analysis for Maintenance Management Method: A Quantitative Graphical Analysis to Support Maintenance Management Decision Making, Quality and Reliability Engineering International 2012. DOI: 10.1002/qre.1296
Cheng S., Azarian M., Pecht M. (2010), Sensor Systems for Prognostics and Health Management, Sensors, 10, 5774-5797; doi:10.3390/s100605774.
Crespo A, Gupta, J. (2006) Contemporary maintenance management: process, framework and supporting pillars. Omega, vol. 34(3), pp. 313–26.
Gómez, J.F., Crespo, A. (2012), Maintenance Management in Network Utilities, frame-work and practical implementation, Springer-Verlag, London
Gonzalez-Prida, V., Crespo, A., Barberá, L. (2012).New Services Generated by the E-Technology Applied on the Warranty Management.International Journal of E- Business Development, vol. 2, 1, pp. 16-22
Guillén A.J., Gómez J., Crespo A., Guerrero A., Sola A. (2013), Advances in PHM application frameworks: Processing Methods, Prognosis Models, Decision Making, Chemical Engineering Transactions, vol 33, 2013.
Gupta, J., Trinquier, C., Lorton, A., Feuillard, V. (2012), Characterization of prognosis methods: an industrial approach. European Conference of the Prognostics and Health Management Society 2012.
Hausladen, I., Bechheim, C. (2004) E-maintenance platform as a basis for business process integration. Proceedings of INDIN04, second IEEE international conference on industrial informatics, Berlin, Germany, 2004. p. 46– 51.
International Standards Organization (ISO) (2004). Condition Monitoring and Diagnostics of Machines - Prognostics part 1: General Guidelines. In ISO, ISO13381-1:2004. Genève, Switzerland: International Standards Organization.
Industrial automation Systems and integration (ISO).Diagnostics, capability assessment and maintenance applications integration. Part I: overviewand general requirements. In ISO, ISO 18435-1:2009. Genève, Switzerland: International Standards Organization.
Industrial automation Systems and integration (ISO). Condition monitoring and diagnostics of machines: General guidelines. In ISO, ISO 17359:2011.Genève, Switzerland: International Standards Organization.
Industrial automation Systems and integration (ISO). Petroleum, petrochemical and natural gas industries: Collection and exchange of reliability and maintenance data for equipment. In ISO, ISO/DIS 14224:2004.Genève, Switzerland: International StandardsOrganization.
Jardine, A., Lin, D., Banjevic, D., (2006). A review onmachinery diagnostics and prognostics implementing condition based maintenance. MechSyst Signal Process, vol. 20, pp.1483–1510.
Kans M., (2009). The advancement of maintenance information technology.A literature review.Journal of Quality in Maintenance Engineering, vol. 15 (1), pp. 5– 16.
Lee,J, Ni, J. (2004). Infotronics-based intelligent maintenance system and its impacts to closed-loop product life cycle systems. Invited keynote paper for IMS’2004—International conference on intelligent maintenance systems, Arles, France, 2004.
Lee, J., Ni, J., Djurdjanovic, D., Qiu, H., Liao, H (2006), Intelligent prognostics tools and e-maintenance.Computers in Industry, vol 57, pp. 476–489
López-Campos, M., Crespo, A., Gómez, J.F. (2013), Modelling using UML and BPMN the integration of open reliability, maintenance and condition monitoring management systems. Computers in Industry, vol. 64, p.p. 524–542.
López-Campos, M., Crespo, A. (2011), Modelling a maintenance management framework based on PAS 55 standard, Quality and Reliability Engineering International, vol. 27 (6), pp. 805–820.
Ly, C., Tom, K, Byington, C.S., Patrick, R., Vatchsevanos,G. J. (2009). Fault Diagnosis an Failure Prognosis on Engeneering System: a Global Perspective. 5th Annual IEEE Conference on Automation Science and Engineering Bangalore, India,
Moubray J, (1997),RCM II: Reliability-centred Maintenance. New York: Industrial Press Inc.
Muller, A., Crespo, A., Iung, B. (2008). On the concept of e-maintenance: Review and current research, Reliability Engineering and System Safety, vol. 93, pp. 1165–1187
Pecth, M., Rubyca, J. (2010).A prognostics and health management roadmap for information and electronics- rich systems, Microelectronics Reliability, vol. 50, pp. 317–323.
Rasovska, I, Chebel-Morello, B., Zerhouni, N. (2005). Process of s-maintenance: decision support system for maintenance intervention. In: Proceedings of the 10th IEEE conference on emerging technologies and factory automation, vol. 2, pp. 679–86
Waeyenbergh, G., Pintelon, L., 2009. CIBOCOF: a framework for industrial maintenance concept development. International Journal of Production Economics,121, 633–640 2009.
Takata S, Kimura F, Van Houten FJAM, Westkämper E, Shpitalni M, Ceglarek D, Lee J. (2004), Maintenance: Changing Role in Life Cycle Management, Annals of the CIRP, 53/2, 643 – 656.
Sheppard J, Kaufman M, Wilmering T (2008), IEEE Standards for Prognostics and Health Management, IEEE AUTOTESTCON 2008.
Sun, B., Zeng S., Kang, R., Pecht, M., (2012), Benefits and Challenges of System Prognostics, IEEE Transactions on Reliabilty,vol 61(2), pp 323-335
Kunche S., Chen Ch.,Pecht M. (2012), A review of PHM system’s architectural frameworks MFPT 2012: The Prognostics and Health Management Solutions Conference,24-26 April, Dayton, OH, 2012
Tobon-Mejia, D., Medjaher, K., Zerhouni N. (2010). The ISO 13381-1 Standard's Failure Prognostics Process Through an Example. IEEE Prognostics & System Health Management Conference, PHM'2010., Macau (China) Vachtsevanos, G., Lewis, F. L., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent fault diagnosis and prognosis for engineering system. Hoboken, NJ: John Wiley & Sons, Inc. Zio E., Di Maio F. (2010). A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system, Reliability Engineering and System Safety 95, 49–57
Section
Technical Papers

Similar Articles

<< < 1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.