Nanjing University of Science and Technology
Prognostics research has attracted much attention due to its wide range of application objects, advanced technical theory and high practical value. In this paper, from the perspective of "literature tracking", we try to mining and analyze the knowledge structure, distribution context and research hotspots of prognostics, which would be a new attempt to the research of prognostics review. The results show that: (1) in terms of knowledge structure, prognostics has strong coupling correlation with condition monitoring and health management. Model driven, knowledge driven, statistical driven, probabilistic reasoning methods, machine learning and deep learning are the key technical categories of prognostics. (2) In terms of hotspots migration, the research on prognostics mainly includes four periods: theoretical foundation period, connotation extension period, technology emergence period and method integration period. This paper summarizes the current achievements, difficulties faced and development contributions, clarifies the development track and practical effect of prognostics theory, and provides a clear direction for the step development of prognostics in the future, which quality improvement for collected big data, online prediction and migration learning will be the main trends in future development.