Challenges and Opportunities of System-Level Prognostics

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 22; p. 7655
• Main Authors: Kim, Seokgoo, Choi, Joo-Ho, Kim, Nam H.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 18.11.2021; MDPI
• Subjects: Algorithms; challenges; dependency; Mathematical models; multiple components; Physics; remaining useful life; Review; Sensors; Survival analysis; system-level prognostics
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21227655

Prognostics and health management (PHM) has become an essential function for safe system operation and scheduling economic maintenance. To date, there has been much research and publications on component-level prognostics. In practice, however, most industrial systems consist of multiple components that are interlinked. This paper aims to provide a review of approaches for system-level prognostics. To achieve this goal, the approaches are grouped into four categories: health index-based, component RUL-based, influenced component-based, and multiple failure mode-based prognostics. Issues of each approach are presented in terms of the target systems and employed algorithms. Two examples of PHM datasets are used to demonstrate how the system-level prognostics should be conducted. Challenges for practical system-level prognostics are also addressed.