Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Hamzeh Soltanali"'
Autor:
Balduíno César Mateus, Mateus Mendes, José Torres Farinha, António Marques Cardoso, Rui Assis, Hamzeh Soltanali
Publikováno v:
Production and Manufacturing Research: An Open Access Journal, Vol 11, Iss 1 (2023)
ABSTRACTPredictive maintenance strategies are becoming increasingly more important with the increased needs for automation and digitalization within pulp and paper manufacturing sector.Hence, this study contributes to examine the most efficient pre-p
Externí odkaz:
https://doaj.org/article/4ba9bc2b97414cd9bd45be3410c1a5cd
Publikováno v:
Energies, Vol 14, Iss 22, p 7758 (2021)
Process integrity, insufficient data, and system complexity in the automotive manufacturing sector are the major uncertainty factors used to predict failure probability (FP), and which are very influential in achieving a reliable maintenance program.
Externí odkaz:
https://doaj.org/article/a166a252837a4dd69c73afc1de631605
Publikováno v:
Information Processing in Agriculture, Vol 2, Iss 2, Pp 101-108 (2015)
Livestock units are known as one of the most influential sectors in the environment pollution. Therefore, the aim of this study was to investigate the environmental impacts of milk production in Guilan province of Iran through Life Cycle Assessment (
Externí odkaz:
https://doaj.org/article/21d3a14c00144c89835ed276a1aa863f
Publikováno v:
Quality Technology & Quantitative Management. 20:256-278
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::21df22c672d8591d660acc9464c0a751
https://doi.org/10.1080/16843703.2022.2093565
https://doi.org/10.1080/16843703.2022.2093565
Publikováno v:
Operations Management Research.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bb7c635bd1d8fc828fd77c88f5c43141
https://doi.org/10.1007/s12063-023-00348-1
https://doi.org/10.1007/s12063-023-00348-1
Autor:
Hamzeh Soltanali, Saeed Ramezani
Publikováno v:
Advances in Reliability, Failure and Risk Analysis ISBN: 9789811999086
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0384e7c5db4cd6a7b55c58561af7f776
https://doi.org/10.1007/978-981-19-9909-3_7
https://doi.org/10.1007/978-981-19-9909-3_7
Publikováno v:
Forschung im Ingenieurwesen. 86:105-122
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7e1d23773fa0815c84515226cd7020db
https://doi.org/10.1007/s10010-021-00568-6
https://doi.org/10.1007/s10010-021-00568-6
Autor:
Abbas Rohani, Aditya Parida, José Torres Farinha, Hamzeh Soltanali, Mohammad Hossein Abbaspour-Fard
Publikováno v:
International Journal of System Assurance Engineering and Management. 11:236-251
Automotive industries require effective and reliable maintenance strategies to ensure high levels of availability and safety. Risk-based maintenance approach is a useful tool for maintenance decision making with the aim of reducing the overall risk i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c2872d622b6c104feb8fb5db4e711c4b
https://doi.org/10.1007/s13198-019-00927-1
https://doi.org/10.1007/s13198-019-00927-1
Publikováno v:
Energies, Vol 14, Iss 7758, p 7758 (2021)
Energies; Volume 14; Issue 22; Pages: 7758
Energies; Volume 14; Issue 22; Pages: 7758
Process integrity, insufficient data, and system complexity in the automotive manufacturing sector are the major uncertainty factors used to predict failure probability (FP), and which are very influential in achieving a reliable maintenance program.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::69ec920652f1e2c647a2c0e74dfffd02
https://www.mdpi.com/1996-1073/14/22/7758
https://www.mdpi.com/1996-1073/14/22/7758
Knowledge-based approaches are useful alternatives to predict the Failure Probability (FP) coping with the insufficient data, process integrity, and complexity issue in manufacturing systems. This study proposes a Fault Tree Analysis (FTA) approach a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3ff304ed5313ac5fc7f3677d0391e62b
https://doi.org/10.20944/preprints202108.0501.v1
https://doi.org/10.20944/preprints202108.0501.v1