Zobrazeno 1 - 10
of 199
pro vyhledávání: '"Piero Baraldi"'
Autor:
Sameer Al-Dahidi, Manoharan Madhiarasan, Loiy Al-Ghussain, Ahmad M. Abubaker, Adnan Darwish Ahmad, Mohammad Alrbai, Mohammadreza Aghaei, Hussein Alahmer, Ali Alahmer, Piero Baraldi, Enrico Zio
Publikováno v:
Energies, Vol 17, Iss 16, p 4145 (2024)
The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling and grid management. This paper presents a comprehensive review conducted with reference to a pioneer
Externí odkaz:
https://doaj.org/article/11726c5ba9c34242b1e8d698d5aa3d12
Publikováno v:
Energies, Vol 17, Iss 10, p 2424 (2024)
We propose a method for selecting the optimal set of weather features for wind energy prediction. This problem is tackled by developing a wrapper approach that employs binary differential evolution to search for the best feature subset, and an ensemb
Externí odkaz:
https://doaj.org/article/5f323b0ff7d04404bed9603b90b4a8b1
Publikováno v:
Energies, Vol 16, Iss 4, p 1610 (2023)
This Special Issue includes seven extended works that have been selected from papers presented at the ESREL 2020 PSAM 15 Conference, the 30th European Safety and Reliability Conference (ESREL 2020) and the 15th Probabilistic Safety Assessment and Man
Externí odkaz:
https://doaj.org/article/0420437135a4483a93df0dba1500c116
Publikováno v:
Energies, Vol 14, Iss 20, p 6743 (2021)
The life cycle of wind turbines depends on the operation and maintenance policies adopted. With the critical components of wind turbines being equipped with condition monitoring and Prognostics and Health Management (PHM) capabilities, it is feasible
Externí odkaz:
https://doaj.org/article/3d182fa22df44df4a334e2174b5089c9
Publikováno v:
Energies, Vol 14, Iss 18, p 6000 (2021)
This work proposes a data-driven methodology for identifying critical components in Complex Technical Infrastructures (CTIs), for which the functional logic and/or the system structure functions are not known due the CTI’s complexity and evolving n
Externí odkaz:
https://doaj.org/article/0932ce0012e14f4993a7cbe3841bcdf9
Publikováno v:
International Journal of Prognostics and Health Management, Vol 10, Iss 4 (2019)
The heart of prognostics and health management (PHM) is to predict the equipment degradation evolution and, thus, its Remaining Useful Life (RUL). These predictions drive the decisions on the equipment Operation and Maintenance (O&M), and these in tu
Externí odkaz:
https://doaj.org/article/ba55a17ee89c4053835e6f5db932e86f
Autor:
Maria Rosaria Termite, Piero Baraldi, Sameer Al-Dahidi, Luca Bellani, Michele Compare, Enrico Zio
Publikováno v:
Energies, Vol 13, Iss 2, p 399 (2020)
The authors would like to add the following note to Figure 3 of their paper published in Energies [...]
Externí odkaz:
https://doaj.org/article/252f88b109284b5583bd96c7ea9a4d8a
Publikováno v:
International Journal of Prognostics and Health Management, Vol 9, Iss 1 (2018)
Sensor data validation has become an important issue in the operation and control of energy production plants. An undetected sensor malfunction may convey inaccurate or misleading information about the actual plant state, possibility leading to unnec
Externí odkaz:
https://doaj.org/article/12a43e4055c344e891258c1c51f29dcf
Publikováno v:
Machines, Vol 6, Iss 3, p 34 (2018)
This work presents a method to improve the diagnostic performance of empirical classification system (ECS), which is used to estimate the degradation state of components based on measured signals. The ECS is embedded in a homogenous continuous-time,
Externí odkaz:
https://doaj.org/article/fa21cac9cbd446be93ce29591e756769
Publikováno v:
International Journal of Prognostics and Health Management, Vol 6, Iss 3 (2015)
The objective of the present work is to develop a novel approach for combining in an ensemble multiple base clusterings of operational transients of industrial equipment, when the number of clusters in the final consensus clustering is unknown. A mea
Externí odkaz:
https://doaj.org/article/71cc1dbe70e24d7090aa3f29e469e60b