Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics
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doi.org/10.3390/data6010005 →Countries where authors are citing Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics
This map shows the geographic impact of Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics more than expected).
Fields of papers citing Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics
This network shows the impact of Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics.
About Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics
This paper, published in 2021, received 194 indexed citations . Written by Manuel Arias Chao, Chetan S. Kulkarni, Kai Goebel and Olga Fink covering the research area of Control and Systems Engineering and Safety, Risk, Reliability and Quality. It is primarily cited by scholars working on Control and Systems Engineering (136 citations), Safety, Risk, Reliability and Quality (50 citations) and Artificial Intelligence (35 citations). Published in Data.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/10.3390/data6010005.