A Dynamic Maintenance Strategy for Prognostics and Health Management of Degrading Systems: Application in Locomotive Wheel-sets

Author(s):  
Bin Liu ◽  
Jing Lin ◽  
Liangwei Zhang ◽  
Min Xie
Author(s):  
David He ◽  
Eric Bechhoefer ◽  
Abhinav Saxena

Wind power generating capacity was 239 GW at the end of 2011, with a further 46 GW of installed capacity to be operational by the end of 2012. While only providing 2.8% of the energy produce in the United States, its is anticipated that by 2030, fully 20% of the electrical energy will come from wind. This widespread deployment of industrial wind projects will require a more proactive maintenance strategy in order to be more cost competitive with traditional energy systems, such as natural gas or coal. This will be particularly true for offshore wind projects, where availability of the site for maintenance can be restricted for extend period of time due to weather. Prognostics and Health Management of these assets can improve operational availability while reducing the cost of unscheduled maintenance.


2011 ◽  
Vol 199-200 ◽  
pp. 543-547 ◽  
Author(s):  
Jiang Long ◽  
Wei An Jiang

There is a growing need for improving manufacturing equipments availability to achieve high levels of productivity. As a key complement to CBM and RCM, PHM is becoming a key enabler for achieving cost effective ultra-reliability and availability in tomorrow’s manufacturing equipments at an affordable cost. Based on traditional maintenance strategies, key issues pertaining to PHM application to manufacturing equipments, including health monitoring, diagnostics and prognostics, are discussed in this paper. As an example, a method for dynamic MFOP based maintenance strategy optimization using PHM and RUL estimation is presented.


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

2020 ◽  
Vol 14 ◽  
Author(s):  
Dangbo Du ◽  
Jianxun Zhang ◽  
Xiaosheng Si ◽  
Changhua Hu

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.


Author(s):  
Zhimin Xi ◽  
Rong Jing ◽  
Pingfeng Wang ◽  
Chao Hu

This paper develops a Copula-based sampling method for data-driven prognostics and health management (PHM). The principal idea is to first build statistical relationship between failure time and the time realizations at specified degradation levels on the basis of off-line training data sets, then identify possible failure times for on-line testing units based on the constructed statistical model and available on-line testing data. Specifically, three technical components are proposed to implement the methodology. First of all, a generic health index system is proposed to represent the health degradation of engineering systems. Next, a Copula-based modeling is proposed to build statistical relationship between failure time and the time realizations at specified degradation levels. Finally, a sampling approach is proposed to estimate the failure time and remaining useful life (RUL) of on-line testing units. Two case studies, including a bearing system in electric cooling fans and a 2008 IEEE PHM challenge problem, are employed to demonstrate the effectiveness of the proposed methodology.


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