期刊
IEEE TRANSACTIONS ON RELIABILITY
卷 69, 期 3, 页码 1110-1129出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TR.2019.2957965
关键词
Degradation; Uncertainty; Computational modeling; Hidden Markov models; Data models; Artificial intelligence; Support vector machines; Data-driven; hybrid method; physics-based; prognostics; prognostics and health management (PHM)
Due to the advancements in sensing technologies and computational capabilities, system health assessment and prognostics have been extensively investigated in the literature. Industry has adopted and implemented many advanced system prognostic applications. This article reviews recent research advances and applications in prognostics modeling methods for engineering systems. The reviewed papers are classified into three major areas based on whether the physics of failure knowledge is incorporated for prognostics, i.e., the data-driven, physics-based, and hybrid prognostic methods. The technical merits and limitations of each prognostic method are discussed. This review also summarizes research and technological challenges in engineering system prognostics, and points out future research directions.
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