Keynote speech: Turbofan engine system diagnostics, Prognostics and Health Management — Challenges and opportunities

2017 ◽  
Author(s):  
Ming Cao
Keyword(s):  
Health Management ◽  
Prognostics And Health Management ◽  
Turbofan Engine ◽  
Challenges And Opportunities ◽  
Keynote Speech ◽  
Engine System

scholarly journals Challenges and Opportunities of System-Level Prognostics

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7655
Author(s):  
Seokgoo Kim ◽  
Joo-Ho Choi ◽  
Nam H. Kim
Keyword(s):  
Health Management ◽  
System Level ◽  
Prognostics And Health Management ◽  
Health Index ◽  
System Operation ◽  
Component Level ◽  
Industrial Systems ◽  
Multiple Components ◽  
Challenges And Opportunities ◽  
Essential Function

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.

Design of Prognostics and Health Management System for Turbofan Engine

2014 ◽  
Vol 599-601 ◽  
pp. 2108-2111 ◽  
Author(s):  
Han Qiang Song ◽  
Ben Wei Li ◽  
Tao Sun ◽  
Yong Hua Wang
Keyword(s):  
Management System ◽  
Health Management ◽  
System Development ◽  
Prognostics And Health Management ◽  
Canonical Structure ◽  
Turbofan Engine ◽  
Course Structure ◽  
Key Technologies ◽  
Aero Engine ◽  
Military Mission

The prognostics and health management system of next generation turbofan aero-engine is researched for better adapting military mission needs. The system canonical structure is designed on the basis of GE company open structure. Additionally, the gas path PHM architecture is especially analyzed, and the key technologies in the system are introduced. It gets the technology ways for the realization of the system development. And the work can provide support for the development of the PHM system from the top course structure view.

scholarly journals Challenges and Opportunities of AI-Enabled Monitoring, Diagnosis & Prognosis: A Review

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Zhibin Zhao ◽  
Jingyao Wu ◽  
Tianfu Li ◽  
Chuang Sun ◽  
Ruqiang Yan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Open Source ◽  
Health Management ◽  
Prognostics And Health Management ◽  
Current Development ◽  
Diagnosis And Prognosis ◽  
Tremendous Progress ◽  
Challenges And Opportunities ◽  
Important Position

AbstractPrognostics and Health Management (PHM), including monitoring, diagnosis, prognosis, and health management, occupies an increasingly important position in reducing costly breakdowns and avoiding catastrophic accidents in modern industry. With the development of artificial intelligence (AI), especially deep learning (DL) approaches, the application of AI-enabled methods to monitor, diagnose and predict potential equipment malfunctions has gone through tremendous progress with verified success in both academia and industry. However, there is still a gap to cover monitoring, diagnosis, and prognosis based on AI-enabled methods, simultaneously, and the importance of an open source community, including open source datasets and codes, has not been fully emphasized. To fill this gap, this paper provides a systematic overview of the current development, common technologies, open source datasets, codes, and challenges of AI-enabled PHM methods from three aspects of monitoring, diagnosis, and prognosis.

A Review on Prognostics and Health Management : 2013~2018

2019 ◽  
Vol 19 (1) ◽  
pp. 68-84 ◽  
Author(s):  
Hyun Su Sim ◽  
Jun-Gyu Kang ◽  
Yong Soo Kim
Keyword(s):  
Health Management ◽  
Prognostics And Health Management

Remaining useful life estimation: a review on stochastic process-based approaches

2020 ◽  
Vol 14 ◽  
Author(s):  
Dangbo Du ◽  
Jianxun Zhang ◽  
Xiaosheng Si ◽  
Changhua Hu
Keyword(s):  
Stochastic Process ◽  
Health Management ◽  
Remaining Useful Life ◽  
Future Trends ◽  
Prognostics And Health Management ◽  
Inverse Gaussian ◽  
Life Estimation ◽  
Inverse Gaussian Process ◽  
Pros And Cons ◽  
Useful Life

Background: Remaining useful life (RUL) estimation is the central mission to the complex systems’ prognostics and health management. During last decades, numbers of developments and applications of the RUL estimation have proliferated. Objective: As one of the most popular approaches, stochastic process-based approach has been widely used for characterizing the degradation trajectories and estimating RULs. This paper aimed at reviewing the latest methods and patents on this topic. Methods: The review is concentrated on four common stochastic processes for degradation modelling and RUL estimation, i.e., Gamma process, Wiener process, inverse Gaussian process and Markov chain. Results: After a briefly review of these four models, we pointed out the pros and cons of them, as well as the improvement direction of each method. Conclusion: For better implementation, the applications of these four approaches on maintenance and decision-making are systematically introduced. Finally, the possible future trends are concluded tentatively.

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