Artificial intelligence in prognostics and health management of engineering systems

2022 ◽  
Vol 108 ◽  
pp. 104552
Author(s):  
Sunday Ochella ◽  
Mahmood Shafiee ◽  
Fateme Dinmohammadi
Keyword(s):  
Artificial Intelligence ◽  
Health Management ◽  
Prognostics And Health Management ◽  
Engineering Systems

scholarly journals Uncertainty in PHM

2020 ◽  
Vol 6 (4) ◽  
Author(s):  
Shankar Sankararaman ◽  
Sankaran Mahadevan ◽  
Marcos E. Orchard
Keyword(s):  
Health Management ◽  
Research Work ◽  
Remaining Useful Life ◽  
Prognostics And Health Management ◽  
Engineering Systems ◽  
Sources Of Uncertainty ◽  
Arduous Task ◽  
Operational Decision Making ◽  
Inaccurate Estimation ◽  
The Impact

Uncertainty plays an important role in diagnostics, prognostics, and health management of engineering systems. The presence of uncertainty leads to an imprecise understanding of the behavior of such systems; as a result, this may adversely affect the results of diagnostics and prognostics. In particular, this may lead to an inaccurate estimation of the remaining useful life, which in turn affects operational decision-making. While several researchers have recognized the importance of uncertainty in prognostics and health management (PHM), there has not been a significant amount of research work that addresses the impact of uncertainty in different PHM activities. This is challenging because there are various sources of uncertainty that affect PHM, their interactions are not fully understood, and therefore, it is an arduous task to perform different PHM activities by systematically accounting for these sources of uncertainty. However, when this can be accomplished, it would be possible to estimate the uncertainty and confidence in the results of diagnostics and prognostics, and quantify the risk involved in prognostics-based decisionmaking.

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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