Prognostics and Energy Efficiency: Survey and Investigations

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

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

Anh HOANG Phuc DO Benoît IUNG Eric LEVRAT Alexandre VOISIN

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 index of an industrial process. In addition, the paper discusses potential prognostic approaches in order to forecast energy efficiency index by underlining difficulties and opportunities to implement such approaches. Finally, a specific example based on an air-fan system is introduced to illustrate energy efficiency concepts and the added value of the prognostics to predict energy efficiency evolution.

How to Cite

HOANG, A., DO, P., IUNG, B., LEVRAT, E., & VOISIN, A. (2014). Prognostics and Energy Efficiency: Survey and Investigations. PHM Society European Conference, 2(1). https://doi.org/10.36001/phme.2014.v2i1.1561
Abstract 39 | PDF Downloads 48

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

Keywords

Prognostic, energy efficiency, Remaining energy-efficient lifetime (REEL), Energy audit

References
Al-mofleh, A. (2009). Prospective of Energy Efficiency Practice , Indicator and Power Supplies Efficiency. Morden applied science, 3, 158–161.
Ang, B. W. (2006). Monitoring changes in economy-wide energy efficiency: From energy–GDP ratio to composite efficiency index. Energy Policy, 34, 574–582.
Ang, B. W., & Xu, X. Y. (2013). Tracking industrial energy efficiency trends using index decomposition analysis. Energy Economics, 40, 1014–1021.
Apostolos, F., Alexios, P., Georgios, P., Panagiotis, S., & George, C. (2013). Energy Efficiency of Manufacturing Processes: A Critical Review. Procedia CIRP, 7, 628–633.
B. lung, M. Veron, M.C. Suhner, A. M. (2005). Integration of Maintenance Strategies into Prognosis Process to Decision-Making Aid on System Operation. CIRP Annals - Manufacturing Technology, 54.
Balaban, E., Narasimhan, S., Daigle, M. J., Roychoudhury, I., Sweet, A., Bond, C., … Gorospe, G. (2013). Development of a Mobile Robot Test Platform and Methods for Validation of Prognostics-Enabled Decision Making Algorithms. International Journal of Prognostics and Health Management, 4, 1–19.
Boardman, B. (2004). Achieving energy efficiency through product policy: the UK experience. Environmental Science & Policy, 7, 165–176.
Bor, Y. J. (2008). Consistent multi-level energy efficiency indicators and their policy implications. Energy Economics, 30, 2401–2419.
Boyd, G. a. (2014). Estimating the changes in the distribution of energy efficiency in the U.S. automobile assembly industry. Energy Economics, 42, 81–87.
Byington, C. S., Roemer, M. J., Kacprzynski, G. J., & Drive, T. P. (2002). Prognostic Enhancements to Diagnostic Systems for Improved Condition-Based Maintenance 1.
Centre, R., Cddex, P., & April, R. (1992). Energy signature models for commercial buildings : test with measured data and interpretation. Energy and Buildings, 19, 143–154.
Chiach, J., Chiach, M., Saxena, A., Rus, G., & Goebel, K. (2013). An Energy-Based Prognostic Framework to Predict Fatigue Damage Evolution in Composites. Annual Conference of the Prognostics and Health Management Society,, 1–9.
Chirarattananon, S., Chaiwiwatworakul, P., Hien, V. D., Rakkwamsuk, P., & Kubaha, K. (2010). Assessment of energy savings from the revised building energy code of Thailand. Energy, 35, 1741–1753.
CML Northern Blower Incorporated. (1991). Fanfacts (p. 11).
Cocheteux, P., Voisin, A., Levrat, E., & Iung, B. (2010). System performance prognostic : context , issues and requirements. 1st IFAC Workshop on Advanced Maintenance Engineering, Services and Technology (AMEST’10).
Dai, J., Das, D., Ohadi, M., & Pecht, M. (2013). Reliability risk mitigation of free air cooling through prognostics and health management. Applied Energy, 111, 104–112.
Darabnia, B., & Demichela, M. (2013). Data Field for Decision Making in Maintenance Optimization : An Opportunity for Energy Saving. Chemical engineering transactions, 33, 367–372.
Dixon, R. K., McGowan, E., Onysko, G., & Scheer, R. M. (2010). US energy conservation and efficiency policies: Challenges and opportunities. Energy Policy, 38, 6398–6408.
Do Van, P., Voisin, A., Levrat, E., & Iung, B. (2013). Remaining useful life based maintenance decision making for deteriorating systems with both perfect and imperfect maintenance actions. In 2013 IEEE Conference on Prognostics and Health Management (PHM) (pp. 1–9). IEEE.
Dragomir, O. E., Gouriveau, R., Dragomir, F., Minca, E., & Zerhouni, N. (2009). Review of Prognostic Problem in Condition-Based Maintenance.
European Commission. (2013). Energy challenges and policy.
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. Reliability Engineering & System Safety, 123, 63–72.
Farla, J. C. ., & Blok, K. (2000). The use of physical indicators for the monitoring of energy intensity developments in the Netherlands, 1980–1995. Energy, 25, 609–638.
Fernández-Francos, D., Martínez-Rego, D., Fontenla-Romero, O., & Alonso-Betanzos, A. (2013). Automatic bearing fault diagnosis based on one-class ν-SVM. Computers & Industrial Engineering, 64, 357–365.
Fleiter, T., Fehrenbach, D., Worrell, E., & Eichhammer, W. (2012). Energy efficiency in the German pulp and paper industry – A model-based assessment of saving potentials. Energy, 40, 84–99.
Gavankar, S., & Geyer, R. (2010). The rebound effect: State of the Debate and Implications for Energy Efficiency Research. Institute of Energy Efficiency (UCSB), 65.
Giacone, E., & Manco, S. (2012). Energy efficiency measurement in industrial processes. Energy, 38, 331–345.
Goebel, K., Saha, B., & Saxena, A. (2008). A comparison of three data-driven techniques for prognostics. In 62nd Meeting of the Society for Machinery Failure Prevention Technology (pp. 1–13). Virginia Beach.
Gvozdenac-Urosevic, B. (2010). Energy efficiency and GDP. Thermal Science, 14, 799–808.
Hasan, O., & Arif, a. F. M. (2014). Performance and life prediction model for photovoltaic modules: Effect of encapsulant constitutive behavior. Solar Energy Materials and Solar Cells, 122, 75–87.
Hilke, A., & Lisa, R. (2012). Mobilising investment in energy efficiency. © OECD/IEA, 2012.
Hsu, D. (2014). How much information disclosure of building energy performance is necessary? Energy Policy, 64, 263–272.
IEA. (2008). Assessing measures of energy efficiency performance and their application in industry. IEA INFORMATION PAPER.
International Energy agency. (2011). Energy-Efficiency Policy Opportunities for Electric Motor-Driven Systems.
Jollands, N., Waide, P., Ellis, M., Onoda, T., Laustsen, J., Tanaka, K., … Meier, A. (2010). The 25 IEA energy efficiency policy recommendations to the G8 Gleneagles Plan of Action. Energy Policy, 38, 6409–6418.
Jørgensen, S. E. (2010). Ecosystem services, sustainability and thermodynamic indicators. Ecological Complexity, 7, 311–313.
Lambert, J. G., Hall, C. a. S., Balogh, S., Gupta, A., & Arnold, M. (2014). Energy, EROI and quality of life. Energy Policy, 64, 153–167.
Lloyd’s Register. (2012). Implementing the Energy Efficiency Design Index ( EEDI ) Guidance for owners , operators and shipyards. Lloyd’s Register, IMO.
Medjaher, K., Skima, H., & Zerhouni, N. (2014). Condition assessment and fault prognostics of microelectromechanical systems. Microelectronics Reliability, 54, 143–151.
Muller, A., Suhner, M.-C., & Iung, B. (2008). Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system. Reliability Engineering & System Safety, 93, 234–253.
Nicolai, R., & Dekker, R. (1997). A Review of Multi-Component Maintenance Models with Economic Dependence. Reliability and Societal Safety, 411–435.
ODYSSEE database. (2010). ODYSSEE data base.
Oikonomou, V., Becchis, F., Steg, L., & Russolillo, D. (2009). Energy saving and energy efficiency concepts for policy making. Energy Policy, 37, 4787–4796.
Parry, I. W. H., Evans, D., & Oates, W. E. (2013). Are energy efficiency standards justified? Journal of Environmental Economics and Management, 1–22.
Patterson, M. G. (1996). What is energy efficiency ? Concepts , indicators and methodological issues. Energy Polic, 24, 377–390.
Phylipsen, D., Blok, K., Worrell, E., & Beer, J. De. (2002). Benchmarking the energy efficiency of Dutch industry: an assessment of the expected effect on energy consumption and CO2 emissions. Energy Policy, 30, 663–679.
Phylipsen, G. J. M., Blok, K., & Worrell, E. (1997). International comparisons of energy efficiency-Methodologies for the manufacturing industry. Energy Policy, 25, 715–725.
Rooks, J., & Wallace, A. K. (2004). Energy Efficiency Of Variable Speed Drive Systems. Industry Applications Magazine, 10, 1–5.
Rosenquist, G., McNeil, M., Iyer, M., Meyers, S., & McMahon, J. (2006). Energy efficiency standards for equipment: Additional opportunities in the residential and commercial sectors. Energy Policy, 34, 3257–3267.
Saha, B., Goebel, K., Poll, S., & Christophersen, J. (2007). A Bayesian Framework for Remaining Useful Life Estimation. Proceedings of AAAI Fall Symposium: AI for Prognostics (Working Notes), 1–6.
Salonitis, K., & Ball, P. (2013). Energy Efficient Manufacturing from Machine Tools to Manufacturing Systems. Procedia CIRP, 7, 634–639.
Salta, M., Polatidis, H., & Haralambopoulos, D. (2009). Energy use in the Greek manufacturing sector: A methodological framework based on physical indicators with aggregation and decomposition analysis. Energy, 34, 90–111.
Sankararaman, S., Daigle, M., Saxena, A., & Goebel, K. (2013). Analytical algorithms to quantify the uncertainty in remaining useful life prediction. 2013 IEEE Aerospace Conference, 1–11.
Sankararaman, S., & Goebel, K. (2014). An Uncertainty Quantification Framework for Prognostics and Condition-Based Monitoring. 16th AIAA Non-Deterministic Approaches Conference, 1–9.
Satish, B., Member, S., Sarma, N. D. R., & Member, S. (2005). A Fuzzy BP Approach for Diagnosis and Prognosis of Bearing faults in Induction Motors, 1–4.
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2010). Metrics for Offline Evaluation of Prognostic Performance, 1–20.
Schenk, N. J., & Moll, H. C. (2007). The use of physical indicators for industrial energy demand scenarios. Ecological Economics, 63, 521–535.
Scofield, J. H. (2009). Do LEED-certified buildings save energy? Yes, but. Energy and Buildings, 41, 1386–1390.
Seow, Y., & Rahimifard, S. (2011). A framework for modelling energy consumption within manufacturing systems. CIRP Journal of Manufacturing Science and Technology, 4, 258–264.
Si, X.-S., Wang, W., Hu, C.-H., & Zhou, D.-H. (2011). Remaining useful life estimation – A review on the statistical data driven approaches. European Journal of Operational Research, 213, 1–14.
Steuwer, D. S. (2013). Energy Efficiency Governance. doi:10.1007/978-3-658-00681-5
Sudhakara Reddy, B., & Kumar Ray, B. (2011). Understanding industrial energy use: Physical energy intensity changes in Indian manufacturing sector. Energy Policy, 39, 7234–7243.
Tanaka, K. (2008). Assessment of energy efficiency performance measures in industry and their application for policy. Energy Policy, 36, 2887–2902.
Thiede, S., Bogdanski, G., & Herrmann, C. (2012). A Systematic Method for Increasing the Energy and Resource Efficiency in Manufacturing Companies. Procedia CIRP, 2, 28–33.
Trianni, A., & Cagno, E. (2012). Dealing with barriers to energy efficiency and SMEs: Some empirical evidences. Energy, 37, 494–504.
Trianni, A., Cagno, E., Thollander, P., & Backlund, S. (2013). Barriers to industrial energy efficiency in foundries: a European comparison. Journal of Cleaner Production, 40, 161–176.
Tsvetanov, T., & Segerson, K. (2013). Re-evaluating the role of energy efficiency standards: A behavioral economics approach. Journal of Environmental Economics and Management, 66, 347–363.
U.S. Department of Energy Energy Efficiency and Renewable Energy. (1989). Improving Fan System Performance: A Sourcebook for Industry.
Udphzrun, R. D. Q. G. (2001). Monitoring energy efficiency performance in New Zealand: A conceptual and mothodological framework.
Urban, J., & Ščasný, M. (2012). Exploring domestic energy-saving: The role of environmental concern and background variables. Energy Policy, 47, 69–80.
Virtanen, T., Tuomaala, M., & Pentti, E. (2013). Energy efficiency complexities: A technical and managerial investigation. Management Accounting Research, 24, 401–416.
Wang, H. (2002). A survey of maintenance policies of deteriorating systems. European Journal of Operational Research, 139, 469–489.
Weinert, N., Chiotellis, S., & Seliger, G. (2011). Methodology for planning and operating energy-efficient production systems. CIRP Annals - Manufacturing Technology, 60, 41–44.
Wiel, S., Egan, C., & delta Cava, M. (2006). Energy efficiency standards and labels provide a solid foundation for economic growth, climate change mitigation, and regional trade. Energy for Sustainable Development, 10, 54–63.
Worrell, E., Price, L., Martin, N., Farla, J., & Schaeffer, R. (1997). Energy intensity in the iron and steel industry: a comparison of physical and economic indicators. Energy Policy, 25, 727–744.
Wu, L.-M., Chen, B.-S., Bor, Y.-C., & Wu, Y.-C. (2007). Structure model of energy efficiency indicators and applications. Energy Policy, 35, 3768–3777.
Yang, Y., Yu, D., & Cheng, J. (2007). A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM. Measurement, 40, 943–950.
Zhou, P., & Ang, B. W. (2008). Linear programming models for measuring economy-wide energy efficiency performance. Energy Policy, 36, 2911–2916.
Zou, B., Elke, M., Hansen, M., & Kafle, N. (2014). Evaluating air carrier fuel efficiency in the US airline industry. Transportation Research Part A: Policy and Practice, 59, 306–330.
Section
Technical Papers