Zobrazeno 1 - 10
of 371
pro vyhledávání: '"Fault Detection and Diagnosis (FDD)"'
Autor:
Ashraf Alghanmi, Akilu Yunusa-Kaltungo
Publikováno v:
Energy and Built Environment, Vol 5, Iss 6, Pp 911-932 (2024)
Fault detection and diagnosis (FDD) approaches comprise three main pillars: model-based, knowledge-based, and data-driven strategies. Data-driven approaches prioritise operational data and do not necessitate in-depth understanding of the system's bac
Externí odkaz:
https://doaj.org/article/19f07991bd224dbc92745b24a5599f90
Autor:
Mohsen Zargarani, Claude Delpha, Demba Diallo, Anne Migan-Dubois, Chabakata Mahamat, Laurent Linguet
Publikováno v:
IEEE Access, Vol 12, Pp 170418-170436 (2024)
The role of clustering in unsupervised fault diagnosis is significant, but different clustering techniques can yield varied results and cause inevitable uncertainty. Ensemble clustering methods have been introduced to tackle this challenge. This stud
Externí odkaz:
https://doaj.org/article/419645ae36544795a4a346bba9db1f6d
Autor:
Vitaliy Pozdnyakov, Aleksandr Kovalenko, Ilya Makarov, Mikhail Drobyshevskiy, Kirill Lukyanov
Publikováno v:
IEEE Open Journal of the Industrial Electronics Society, Vol 5, Pp 428-440 (2024)
Integrating machine learning into Automated Control Systems (ACS) enhances decision-making in industrial process management. One of the limitations to the widespread adoption of these technologies in industry is the vulnerability of neural networks t
Externí odkaz:
https://doaj.org/article/330a130fa2e9493184d8aaa83e184505
Autor:
Mohammed Tahar Habib Kaib, Abdelmalek Kouadri, Mohamed-Faouzi Harkat, Abderazak Bensmail, Majdi Mansouri
Publikováno v:
IEEE Access, Vol 12, Pp 11470-11480 (2024)
Fault detection and diagnosis (FDD) systems play a crucial role in maintaining the adequate execution of the monitored process. One of the widely used data-driven FDD methods is the Principal Component Analysis (PCA). Unfortunately, PCA’s reliabili
Externí odkaz:
https://doaj.org/article/4de09f34ad044addab9eecd189d8572e
Publikováno v:
Energy Reviews, Vol 3, Iss 2, Pp 100071- (2024)
Recent studies show that artificial intelligence (AI), such as machine learning and deep learning, models can be adopted and have advantages in fault detection and diagnosis for building energy systems. This paper aims to conduct a comprehensive and
Externí odkaz:
https://doaj.org/article/b1e0b3173e394f4c814c7bb84ee3754a
Publikováno v:
Energy Reports, Vol 9, Iss , Pp 4005-4017 (2023)
Nowadays, photovoltaic (PV) energy is considered as one of the most encouraging renewable energy sources. Nevertheless, the power delivered by a PV field is strongly attached to irradiance which undergoes rapid variations depending on the climatic co
Externí odkaz:
https://doaj.org/article/5238a939e18c4cb2b3725b9291009d23
Publikováno v:
Energy Reports, Vol 10, Iss , Pp 3113-3124 (2023)
This paper introduces a pioneering fault diagnosis technique termed Interval Ensemble Learning based on Sine Cosine Optimization Algorithm (IEL- SCOA), tailored to tackle uncertainties prevalent in wind energy conversion (WEC) systems. The approach u
Externí odkaz:
https://doaj.org/article/28b9d441491d499399080fa867d49c93
Autor:
Khadija Attouri, Majdi Mansouri, Mansour Hajji, Abdelmalek Kouadri, Kais Bouzrara, Hazem Nounou
Publikováno v:
Signals, Vol 4, Iss 2, Pp 381-400 (2023)
In this work, an effective Fault Detection and Diagnosis (FDD) strategy designed to increase the performance and accuracy of fault diagnosis in grid-connected photovoltaic (GCPV) systems is developed. The evolved approach is threefold: first, a pre-p
Externí odkaz:
https://doaj.org/article/6d949fa81a404717971f6c8f997ac780
Publikováno v:
Energy Reports, Vol 8, Iss , Pp 14673-14698 (2022)
Worldwide, buildings consume a large amount of energy, and a significant share of this energy is wasted due to system degradation, inappropriate control systems and improper maintenance activities. Fortunately, buildings have become data-intensive, w
Externí odkaz:
https://doaj.org/article/5111eabfe81944169663bf3d7ea3c2e7
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