Consideration of an energy efficiency indicator for maintenance decisionmaking: From definition to prognostic

Consideration of an energy efficiency indicator for maintenance decisionmaking: From definition to prognostic

Hoang Anh Phuc Do Benoît Iung Eric Levrat Alexandre Voisin 

CRAN, Université de Lorraine, UMR, CNRS 7039 Campus Sciences BP 70239 – 54506 Vandoeuvre Cedex

Corresponding Author Email: 
Anh.hoang@univ-lorraine.fr, van-phuc.do@univ-lorraine.fr, benoit.iung@univ-lorraine.fr, eric.levrat@univ-lorraine.fr, alexandre.voisin@univ-lorraine.fr
Page: 
559-578
|
DOI: 
https://doi.org/10.3166/JESA.49.559-578
Received: 
N/A
| |
Accepted: 
N/A
| | Citation

OPEN ACCESS

Abstract: 

The paper presents firstly an overview of various definitions/concepts of energy efficiency and their related applications in different contexts, especially in industrial sectors. Each definition/concept is analyzed and recommended for different decision-making levels. Then a multi-level approach is described in detail for evaluating energy efficiency indicator of an industrial process. The evolution of this indicator with regards to system degradation is studied leading to propose a novel concept named remaining energy-efficient lifetime (REEL). In addition, the paper discusses potential prognostic approaches in order to forecast energy efficiency indicator for calculating this REEL by underlining also difficulties and opportunities to implement such approaches. Finally, a case study on TELMA e-maintenance platform is introduced to illustrate energy efficiency concepts and the added value of the prognostics to predict energy efficiency evolution and REEL.

Keywords: 

energy efficiency, maintenance, prognostic, decision indicators, multi-component system

1. Introduction
2. Concepts de l’efficacité énergétique
3. Formulation et évaluation de la performance d’efficacité énergétique
4. Approche de pronostic de l’efficacité énergétique
5. Validation des concepts EEI (SEC) et REEL au cas d’application de la plateforme TELMA
6. Conclusions
  References

Al-mofleh A. (2009). Prospective of Energy Efficiency Practice, Indicator and Power Supplies Efficiency. Morden Appl. Sci. vol. 3, p. 158–161.

Ang B.W. (2006). Monitoring changes in economy-wide energy efficiency: From energy–GDP ratio to composite efficiency index. Energy Policy vol. 34, p. 574–582.

Ang B.W., Xu X.Y. (2013). Tracking industrial energy efficiency trends using index decomposition analysis. Energy Econ. vol. 40, p. 1014–1021.

Bertoldi P., Rezessy S., Oikonomou V. (2013). Rewarding energy savings rather than energy efficiency: Exploring the concept of a feed-in tariff for energy savings. Energy Policy vol. 56, p. 526–535.

Boardman B. (2004). Achieving energy efficiency through product policy: the UK experience. Environ. Sci. Policy vol. 7, p. 165–176.

Chan D.Y.-L., Huang C.-F., Lin W.-C., Hong G.-B. (2014). Energy efficiency benchmarking of energy-intensive industries in Taiwan. Energy Convers. Manag. vol. 77, p. 216–220.

Do P., Barros A., Bérenguer C., Bouvard K., Brissaud F. (2013). Dynamic grouping maintenance with time limited opportunities. Reliab. Eng. Syst. Saf. vol. 120, p. 51–59.

Do P., Voisin A., Levrat E., Iung B. (2015). Condition-based maintenance with both perfect and imperfect maintenance actions. Reliab. Eng. Syst. Saf. vol. 133, p. 22-32.

Do P., Levrat E., Voisin A., Iung B. (2012). Remaining useful life (RUL) based maintenance decision making for deteriorating systems, 2nd IFAC Workshop on Advanced Maintenance Engineering, Service and Technology (A-Mest’12).

Dragomir O.E., Gouriveau R., Dragomir F., Minca E., Zerhouni N. (2009). Review of Prognostic Problem in Condition-Based Maintenance. European Control Conference, ECC’09, Budapest, Hungary.

Fan J., Yung K.-C., Pecht M. (2014). Prognostics of lumen maintenance for High power white light emitting diodes using a nonlinear filter-based approach. Reliab. Eng. Syst. Saf. 123, p. 63–72.

Fysikopoulos A., Anagnostakis D., Salonitis K., Chryssolouris G. (2012). An Empirical Study of the Energy Consumption in Automotive Assembly, 45th CIRP Conference on Manufacturing Systems.

Gavankar S., Geyer R. (2011). The rebound effect: State of the Debate and Implications for Energy Efficiency Research, Institute of Energy Efficiency (UCSB).

Giacone E., Manco S. (2012). Energy efficiency measurement in industrial processes. Energy vol. 38, p. 331–345.

Goebel K., Saha B., Saxena A. (2008). A comparison of three data-driven techniques for prognostics, 62nd Meeting of the Society for Machinery Failure Prevention Technology.

Gvozdenac-Urosevic B. (2010). Energy efficiency and GDP. Therm. Sci. vol. 14, p. 799–808.

Iung B., Levrat E., Marquez A.C., Erbe H. (2009). Conceptual framework for e-Maintenance: Illustration by e-Maintenance technologies and platforms. Annu. Rev. Control vol. 33, p. 220–229.

Jollands N., Waide P., Ellis M., Onoda T., Laustsen J., Tanaka K., de T’Serclaes P., Barnsley I., Bradley R., Meier A. (2010). The 25 IEA energy efficiency policy recommendations to the G8 Gleneagles Plan of Action. Energy Policy vol. 38, p. 6409–6418.

Lambert, J.G., Hall, C. a. S., Balogh, S., Gupta, A., Arnold, M., (2014). Energy, EROI and quality of life. Energy Policy vol. 64, p. 153–167.

Medjaher K., Skima H., Zerhouni N. (2014). Condition assessment and fault prognostics of microelectromechanical systems. Microelectron. Reliab. vol. 54, p. 143–151.

Muller A., Suhner M.-C., Iung B. (2008). Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system. Reliab. Eng. Syst. Saf. vol. 93, p. 234–253.

Nicolai R., Dekker R. (1997). A Review of Multi-Component Maintenance Models with Economic Dependence. Reliab. Soc. Saf. p. 411–435.

Reviewed P., Riverside C.S. (2013). Electronic Theses and Dissertations UC Riverside Mining Time Series Data : Moving from Toy Problems to Realistic Deployments A Dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science by.

Ritz C., Streibig J.C. (2008). Nonlinear Regression with R. Springer Science & Business Media.

Saidur R. (2010). A review on electrical motors energy use and energy savings. Renew. Sustain. Energy Rev. vol. 14, p. 877–898.

Salonitis K., Ball P. (2013). Energy Efficient Manufacturing from Machine Tools to Manufacturing Systems. Procedia CIRP, vol. 7, p. 634–639.

Sankararaman S., Goebel K. (2014). An uncertainty quantification framework for prognostics and condition-based monitoring. 16th AIAA Non-Deterministic Approaches Conf.

Si X.-S., Wang W., Hu C.-H., Zhou D.-H. (2011). Remaining useful life estimation – A review on the statistical data driven approaches. Eur. J. Oper. Res. vol. 213, p. 1–14.

Steuwer D.S. (2013). Energy efficiency governance: the case of white certificate instruments for energy efficiency in Europe, Springer Fachmedien Wiesbaden, p. 27–48.

Tanaka K. (2008). Assessment of energy efficiency performance measures in industry and their application for policy. Energy Policy vol. 36, p. 2887–2902.

Trianni A., Cagno E., Thollander P., Backlund S. (2013). Barriers to industrial energy efficiency in foundries: a European comparison. J. Clean. Prod. vol. 40, p. 161–176.

Tsvetanov T., Segerson K. (2013). Re-evaluating the role of energy efficiency standards: A behavioral economics approach. J. Environ. Econ. Manage. vol. 66, p. 347–363.

Udphzrun R.D.Q.G. (2001). Monitoring energy efficiency performance in New Zealand: A conceptual and mothodological framework. Energy Efficiency and Conservation Authority, September, New Zealand.

Urban, J., Scasny, M., (2012). Exploring domestic energy-saving: The role of environmental concern and background variables. Energy Policy vol. 47, p. 69–80.

Van Noortwijk J.M. (2009). A survey of the application of gamma processes in maintenance. Reliab. Eng. Syst. Saf. vol. 94, p. 2–21.

Wang H. (2002). A survey of maintenance policies of deteriorating systems. Eur. J. Oper. Res. vol. 139, p. 469–489.