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A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

Author

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  • Adolfo Crespo Márquez

    (Department of Industrial Management, Universidad de Sevilla, 41092 Seville, Spain)

  • Antonio de la Fuente Carmona

    (Department of Industrial Management, Universidad de Sevilla, 41092 Seville, Spain)

  • Sara Antomarioni

    (Department of Industrial Engineering and Mathematical Science, Università Politecnica delle Marche, 60131 Ancona, Italy)

Abstract

In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption efficiency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets’ life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence differs from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of performance and help manage assets’ O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions.

Suggested Citation

  • Adolfo Crespo Márquez & Antonio de la Fuente Carmona & Sara Antomarioni, 2019. "A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency," Energies, MDPI, vol. 12(18), pages 1-25, September.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:18:p:3454-:d:265067
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    1. Zhaoxuan Li & SM Mahbobur Rahman & Rolando Vega & Bing Dong, 2016. "A Hierarchical Approach Using Machine Learning Methods in Solar Photovoltaic Energy Production Forecasting," Energies, MDPI, vol. 9(1), pages 1-12, January.
    2. Chen, Ji-Long & Liu, Hong-Bin & Wu, Wei & Xie, De-Ti, 2011. "Estimation of monthly solar radiation from measured temperatures using support vector machines – A case study," Renewable Energy, Elsevier, vol. 36(1), pages 413-420.
    3. Kans, Mirka & Ingwald, Anders, 2008. "Common database for cost-effective improvement of maintenance performance," International Journal of Production Economics, Elsevier, vol. 113(2), pages 734-747, June.
    4. Claudia Diamantini & Domenico Potena & Emanuele Storti, 2013. "A virtual mart for knowledge discovery in databases," Information Systems Frontiers, Springer, vol. 15(3), pages 447-463, July.
    5. Zio, E., 2009. "Reliability engineering: Old problems and new challenges," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 125-141.
    6. Niu, Gang & Yang, Bo-Suk & Pecht, Michael, 2010. "Development of an optimized condition-based maintenance system by data fusion and reliability-centered maintenance," Reliability Engineering and System Safety, Elsevier, vol. 95(7), pages 786-796.
    7. Hong Zhang & Lixing Chen & Yong Qu & Guo Zhao & Zhenwei Guo, 2014. "Support Vector Regression Based on Grid-Search Method for Short-Term Wind Power Forecasting," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-11, June.
    8. Olivencia Polo, Fernando A. & Ferrero Bermejo, Jesús & Gómez Fernández, Juan F. & Crespo Márquez, Adolfo, 2015. "Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models," Renewable Energy, Elsevier, vol. 81(C), pages 227-238.
    9. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    10. Moutis, Panayiotis & Skarvelis-Kazakos, Spyros & Brucoli, Maria, 2016. "Decision tree aided planning and energy balancing of planned community microgrids," Applied Energy, Elsevier, vol. 161(C), pages 197-205.
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