A Scientometric Methodology Based on Co-Word Analysis in Gas Turbine Maintenance

Abstract

Evaluation of scientific journals has a profound effect on the future of scientific research so that different institutes and countries can set appropriate goals and invest with less risk in various scientific fields. Accordingly, this article presents a new method based on a combination of co-word analysis and social network analysis to extract the hotspot topics. Using HistCite, NodeXL, and VOSviewer, then combining their results, the desired analysis is conducted for six time periods. Based on the bibliographic parameters in HistCite and by defining an index, the first five periods are selected such that both quantity and quality of articles in each period are maximum compared to other years, while the sixth time period contains the latest research. For each of the six periods, the co-word networks as created in VOSviewer are analyzed. Next, based on a combination of network centralities developed in NodeXL, the hotspot keywords are specified which are then validated and aggregated using the bibliographic parameters in HistCite. The results reveal five important time periods in gas turbine maintenance. The hotspot keywords obtained for the last period show that in recent years, some topics including gas turbine fault prognosis, neural network-based approaches, big data analysis, sensor fault diagnosis, blade availability, economic analysis and useful life estimation are prominent subjects in gas turbine maintenance.