Journal publications

2024

[J34] Centeno-Telleria, M., Yue, H., Carroll, J., Aizpurua, J.I., Penalba, M. (2024), O&M-aware techno-economic assessment for floating offshore wind farms: A geospatial evaluation off the North Sea and the Iberian Peninsula, Applied Energy, https://doi.org/10.1016/j.apenergy.2024.123684

[J33] Ugarte, J., Aizpurua, J.I., and Barrenetxea, M. (2024), Uncertainty Distribution Assessment of Jiles-Atherton Parameter Estimation for Inrush Current Studies, IEEE Transactions on Power Delivery, https://doi.org/10.1109/TPWRD.2024.3398790 

[J32] Centeno-Telleria, M., Yue, H., Carroll, J., Penalba, M., Aizpurua, J.I. (2024), Impact of operations and maintenance on the energy production of floating offshore wind farms across the North Sea and the Iberian Peninsula, Renewable Energy, https://doi.org/10.1016/j.renene.2024.120217

[J31] Ramirez, I., Aizpurua, J.I., Lasa, I., Del Rio, L. (2024), Probabilistic feature selection for improved asset lifetime estimation in renewables. Application to transformers in photovoltaic power plants, Engineering Applications of Artificial Intelligence, https://doi.org/10.1016/j.engappai.2023.107841

2023

[J30] Centeno-Telleria, M., Aizpurua, J.I., Penalba, M. (2023), Computationally Efficient Analytical O&M Model for Strategic Decision-Making in Offshore Renewable Energy Systems, Energy, https://doi.org/10.1016/j.energy.2023.129374

[J29] Konuk, E. B., Centeno-Telleria, M., Zarketa-Astigarraga, A., Aizpurua, J. I., Giorgi, G., Bracco, G., & Penalba, M. (2023). On the Definition of a Comprehensive Technology-Informed Accessibility Metric for Offshore Renewable Energy Site Selection. Journal of Marine Science and Engineering, 11(9), 1702.  https://doi.org/10.3390/jmse11091702

[J28] Aizpurua, J.I, Peña-Alzola, R, Olano, J., Ramirez, I., Lasa, I., Del Rio, L. & Dragicevic, T. (2023), Probabilistic machine learning aided transformer lifetime prediction framework for wind energy systems, International Journal of Electrical Power & Energy Systems, https://doi.org/10.1016/j.ijepes.2023.109352

[J27] Aizpurua, J.I, Knutsen, K. E., Heimdal, M. & Vanem, E. (2023), Integrated machine learning and probabilistic degradation approach for vessel electric motor prognostics, Ocean Engineering, https://doi.org/10.1016/j.oceaneng.2023.114153.

[J26] Barber, S., Izagirre,  U., Serradilla, O., Olaizola, J., Zugasti, E., Aizpurua, J.I., Milani, A., Sehnke, F., Sakagami, Y., Henderson, C., (2023), Best Practice Data Sharing Guidelines for Wind Turbine Fault Detection Model Evaluation, Energies, https://doi.org/10.3390/en16083567

2022

[J25] Aizpurua, J.I, Ramirez, I., Lasa, I., del Rio, L., Ortiz, A. & Stewart, B. G. (2022), Hybrid Transformer Prognostics Framework for Enhanced Probabilistic Predictions in Renewable Energy Applications, IEEE Transactions on Power Delivery, https://doi.org/10.1109/TPWRD.2022.3203873. Link, Accepted Paper

[J24] Penalba, M., Aizpurua, J.I, Martinez, A. & Iglesias, G. (2022), A data-driven long-term metocean data forecasting approach for the design of marine renewable energy systems, Renewable and Sustainable Energy Reviews, https://doi.org/10.1016/j.rser.2022.112751. Link

[J23] Aizpurua, J.I, Stewart, B. G., McArthur, S. D. J.,  Penalba, M., Barrenetxea, M., Muxika, E. and Ringwood,  J. V. (2022), Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study, Reliability Engineering & System Safety, https://doi.org/10.1016/j.ress.2022.108676. Link

[J22] Aizpurua, J. I., Penalba, M.,  Kirillova, N., Lekube, J. and Marina, D. (2022), Context-informed conditional anomaly detection approach for wave power plants: The case of air turbines, Ocean Engineering, 10.1016/j.oceaneng.2022.111196. Link

2021

[J21] Penalba, M.,  Aizpurua, J. I., and Martinez-Perurena, A. (2021), On the definition of a risk index based on long-term metocean data to assist in the design of Marine Renewable Energy systems, Ocean Engineering, 10.1016/j.oceaneng.2021.110080. Link

2020

[J20] Aizpurua, J. I., Stewart, B., McArthur S., Kearns, M., Jajware, N., Garro, U., Muxika, E. and Mendikute, M. (2020), A Diagnostics Framework for Underground Power Cables Lifetime Estimation Under Uncertainty, IEEE Transactions on Power Delivery, 10.1109/TPWRD.2020.3017951. Accepted Paper

[J19] Chiacchio, F., Aizpurua, J.I., L. Compagno, and D. D’Urso (2020). SHyFTOO, an object-oriented Monte Carlo simulation library for the modeling of Stochastic Hybrid Fault Tree Automaton. Expert Systems with Applications, 146, 113139. doi: https://doi.org/10.1016/j.eswa.2019.113139. Link

2019

[J18] Aizpurua, J.I., B. Stewart, S. McArthur, B. Lambert, J. Cross, and V. Catterson (2019). Improved Power Transformer Condition Monitoring under Uncertainty through Soft Computing and Probabilistic Health Index. Applied Soft Computing. doi: https://doi.org/10.1016/j.asoc.2019.105530.

[J17] Chiacchio, F., Aizpurua, J. I., L. Compagno, S. M. Khodayee, and D. D’Urso (2019). Modelling and Resolution of Dynamic Reliability Problems by the Coupling of Simulink and the Stochastic Hybrid Fault Tree Object Oriented (SHyFTOO) Library. MDPI Information. 10(9). doi: 10.3390/info10090283.

[J16] Garro, U., E. Muxika, Aizpurua, J.I., and M. Mendikute (2019). FPGA Based Degradation and Reliability Monitor for Undergroud Cables. MDPI Sensors. 19(9). doi: 10.3390/s19091995.

[J15] Garro, U., E. Muxika, Aizpurua, J.I., and M. Mendikute (2019). FPGA-Based Stochastic Activity Networks for On-Line Reliability Monitoring. IEEE Transactions on Industrial Electronics. doi: 10.1109/TIE.2019.2928244.

2018

[J14] Aizpurua, J.I., S. McArthur, B. Stewart, B. Lambert, J. Cross, and V. Catterson (2018). Adaptive Power Transformer Lifetime Predictions through Machine Learning & Uncertainty Modelling in Nuclear Power Plants. IEEE Transactions on Industrial Electronics. doi: 10.1109/TIE.2018.2860532.

[J13] Aizpurua, J.I., Y. Papadopoulos, and G. Merle (2018). Explicit modelling and treatment of repair in prediction of dependability. IEEE Transactions on Dependable and Secure Computing. doi: 10.1109/TDSC.2018.2857810.

[J12] Chiacchio, F., D. D’Urso, F. Famoso, S. Brusca, Aizpurua, J.I., and V. Catterson (2018). On the use of dynamic reliability for an accurate modelling of renewable power plants. Energy. 151, 605–621. doi: https://doi.org/10.1016/j.energy.2018.03.101.

[J11] Chiacchio, F., F. Famoso, D. D’Urso, S. Brusca, Aizpurua, J.I., and L. Cedola (2018). Dynamic Performance Evaluation of Photovoltaic Power Plant by Stochastic Hybrid Fault Tree Automaton Model. Energies. 11(2). doi: 10.3390/en11020306.

[J10] Aizpurua, J. I., Catterson, V.M., Stewart, B. G., McArthur, S. D. J., Lambert, B. and Cross, J. G (2018). Uncertainty-Aware Fusion of Probabilistic Classifiers for Improved Transformer Diagnostics. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 1–13. doi: 10.1109/TSMC.2018.2880930.

[J09] Kabir, S., M. Yazdi, Aizpurua, J.I., and Y. Papadopoulos (2018). Uncertainty Analysis of Complex Dynamic Systems through Fuzzy Sets and Petri Nets. IEEE Access. 6, 29499–29515. doi: 10.1109/ACCESS.2018.2843166.

[J08] Aizpurua, J.I., V. Catterson, B. Stewart, S. McArthur, B. Lambert, B. Ampofo, G. Pereira, and J. Cross (Apr. 2018). Power Transformer Dissolved Gas Analysis through Bayesian Networks and Hypothesis Testing. IEEE Transactions on Dielectrics and Electrical Insulation. 25(2), 494–506. doi: 10.1109/TDEI.2018.006766.

2017

[J07] Chiacchio, F., Aizpurua, J.I., D. D’Urso, and L. Compagno (2017). Coherence region of the Priority-AND gate: Analytical and numerical examples. Wiley Quality and Reliability Engineering International. 34(1), 107–115. doi: 10.1002/qre.2241.

[J06] Aizpurua, J.I., V. M. Catterson, Y. Papadopoulos, F. Chiacchio, and G. Manno (Sept. 2017). Improved Dynamic Dependability Assessment Through Integration With Prognostics. IEEE Transactions on Reliability. 66(3), 893–913. doi: 10.1109/TR.2017.2693821.

[J05]Aizpurua, J.I., V. Catterson, Y. Papadopoulos, F. Chiacchio, and D. D’Urso (Aug. 2017). Supporting group maintenance through prognostics-enhanced dynamic dependability prediction. Reliability Engineering & System Safety. doi: http://doi.org/10.1016/j.ress.2017.04.005.

[J04]Aizpurua, J.I., V. M. Catterson, I. F. Abdulhadi, and M. Garcia (Apr. 2017). A Model-Based Hybrid Approach for Circuit Breaker Prognostics Encompassing Dynamic Reliability and Uncertainty. IEEE Transactions on Systems, Man, and Cybernetics: Systems. PP(99), 1–12. doi: 10.1109/TSMC.2017.2685346.

[J03]Aizpurua, J. I., Y. Papadopoulos, E. Muxika, F. Chiacchio, and G. Manno (Jan. 2017). On Cost-effective Reuse of Components in the Design of Complex Reconfigurable Systems. Wiley Quality and Reliability Engineering International. doi: 10.1002/qre.2112.

2016

[J02] Aizpurua, J. I., E. Muxika, Y. Papadopoulos, F. Chiacchio, and G. Manno (2016). Application of the D3H2 Methodology for the Cost-Effective Design of Dependable Systems. Safety. 2(2). doi: 10.3390/safety2020009.

2013

[J01] Aizpurua, J. I. and E. Muxika (2013). Model Based Design of Dependable Systems: Limitations and Evolution of Analysis and Verification Approaches. International Journal on Advances in Security. 6, 12–31.