Abstract
Failure control based on condition monitoring needs to be sustainable and well-structured, and to rely on procedures fulfilling international standards, in order to keep and improve solutions over time. Failure detection and prediction in networks of assets demands an even more sophisticated approach and a clear conceptual framework, to be able to consider individual assets degradation behaviours and corresponding integrated effects on the network. In these cases, logic of failure control has to manage not only reliability data but also operation and real time internal and locational variables. Cyber-Physical Systems (CPS) approach easies the integrations of physical processes, network of assets and intelligent computation; CPS may enable co-operation among autonomous and distributed intelligence. Because of all these reasons this chapter sustains that failure detection and prediction in networks of assets can seriously benefit of CPS. However, CPS implementation needs a conceptual framework allowing the permanent development of current and new algorithms for advanced asset degradation and production forecasting. Multi-Agent System (MAS) architecture complies with these framework requirements from the scalability point of view, but in order to cope with these solutions adaptation to locational and operational changes, artificial neural networks (ANN) are developed in this chapter on top of the legacy supervisory control and data acquisition system, to implement an innovative failure detection and power generation forecasting method. The model and method is demonstrated in grid connected solar photovoltaic power plants.
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Acknowledgements
This research is included within the project “Desarrollo de Procesos Avanzados de Operación y Mantenimiento Sobre Sistemas Cibero Fïsicos (Cps) en el Ámbito de la Industria 4.0”, DPI2015-70842-R, funded by Spanish Goverment, Ministery of Economics and Competiviness. It is also was performed within the context of Sustain Owner (‘Sustainable Design and Management of Industrial Assets through Total Value and Cost of Ownership’), a project sponsored by the EU Framework Program Horizon 2020, MSCA-RISE-2014: Marie Skłodowska-Curie Research and Innovation Staff Exchange (RISE) (grant agreement number 645733—Sustain-Owner—H2020-MSCA-RISE-2014).
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Ferrero Bermejo, J., Gómez Fernández, J.F., Guillén López, A.J., Olivencia Polo, F., Crespo Márquez, A., González-Prida Díaz, V. (2020). A CPS for Condition Based Maintenance Based on a Multi-agent System for Failure Modes Prediction in Grid Connected PV Systems. In: Crespo Márquez, A., Macchi, M., Parlikad, A. (eds) Value Based and Intelligent Asset Management. Springer, Cham. https://doi.org/10.1007/978-3-030-20704-5_8
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