Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Pizza, Gianmarco"'
Publikováno v:
PHM Society European Conference. 7:530-540
Best Paper Award Lizenzangabe: CC BY 3.0 United States
Quantifying the predictive uncertainty of a model is an important ingredient in data-driven decision making. Uncertainty quantification has been gaining interest especially for deep learning
Quantifying the predictive uncertainty of a model is an important ingredient in data-driven decision making. Uncertainty quantification has been gaining interest especially for deep learning
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::37dd00753def4cd4877248ca535b2911
https://doi.org/10.36001/phme.2022.v7i1.3342
https://doi.org/10.36001/phme.2022.v7i1.3342
Publikováno v:
In Catalysis Today 2010 155(1):123-130
The Supervisory Control and Data Acquisition (SCADA) system installed on every wind turbine collects performance and condition data from various components of the turbine in time intervals of 10 minutes. The data is stored and has been used primarily
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3760::c67699672db9b382ec195496c470b694
https://hdl.handle.net/11475/22923
https://hdl.handle.net/11475/22923
Autor:
Pizza, Gianmarco, Mantzaras, John, Frouzakis, Christos E., Tomboulides, Ananias G., Boulouchos, Konstantinos
Publikováno v:
In Proceedings of the Combustion Institute 2009 32(2):3051-3058
We demonstrate the deployment of a novel deep learning algorithm enabling smart maintenance of wind turbines based on 10 minute SCADA data. The newly developed algorithm has the following advantages over existing solutions: • The algorithms are bas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3760::009032ee44cdddfddd5023bf6033fc09
https://hdl.handle.net/11475/21401
https://hdl.handle.net/11475/21401
Autor:
Goren Huber, Lilach, Pizza, Gianmarco
Aus einer kürzlich erfolgten Zusammenarbeit zwischen Nispera, EKZ und dem Smart Maintenance Team der ZHAW ist ein neues, auf Deep-Learning-Algorithmen basierendes Softwaremodul zur vorausschauenden Wartung entstanden. Die Algorithmen sind in der Lag
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3760::eb8145cdcec81b1c2c3070614ff70cd2
https://hdl.handle.net/11475/23776
https://hdl.handle.net/11475/23776
Best Paper Award
Implementing machine learning and deep learning algorithms for wind turbine (WT) fault detection (FD) based on 10-minute SCADA data has become a relevant opportunity to reduce the operation and maintenance costs of wind farms. T
Implementing machine learning and deep learning algorithms for wind turbine (WT) fault detection (FD) based on 10-minute SCADA data has become a relevant opportunity to reduce the operation and maintenance costs of wind farms. T
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::08f94c31e5f5a1b9b275528a853944da
Early fault detection in wind turbines using the widely available SCADA data has been receiving growing interest due to its cost-effectiveness. As opposed to the large variety of fault detection methods based on high resolusion vibration data, the us
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a9b9a21b8bef94a4ae1dc3a51530c80e
https://hdl.handle.net/11475/20433
https://hdl.handle.net/11475/20433
Predictive maintenance is a key element for lowering Operation and Maintenance (O&M) costs of wind turbines. Predictive maintenance models are usually based on drivetrain vibration data or operational timeseries from the Supervisory Control And Data
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3760::3a7d3508d4fcf6d96289404db4cbba18
https://hdl.handle.net/11475/21546
https://hdl.handle.net/11475/21546