HealthMAP - A Model-Based Framework for On-Line Prognostics and Health Management (PHM)

Author(s):  
Ravi Kapadia ◽  
Mark Walker
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.


Author(s):  
Michael J. Roemer ◽  
Carl S. Byington

Based on the results of a successful Phase I and II SBIR program performed by Impact Technologies, a suite of Prognostics and Health Management (PHM) algorithms have been developed for detecting incipient faults in the critical bearings associated with aircraft gas turbine engines. The component-level prognostic approach is presented that utilizes available sensor information from vibration transducers, along with material-level component fatigue models to calculate remaining useful life for the engine’s critical components. Specifically, correlation between the sensed data and fatigue-based damage accumulation models were developed to provide remaining useful life assessments for life limited components. The combination of health monitoring data and model-based techniques provides a unique and knowledge rich capability that can be utilized throughout the bearings’s entire life, using model-based estimates when no diagnostic indicators are present and using the monitored vibration features at later stages when incipient failure indications are detectable, thus reducing the uncertainty in model-based predictions. A description and specific implementation of this prognosis approach with application to high speed bearings is illustrated herein, using gas turbine engine and bearing test rig data as validation for the methods.


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

2010 ◽  
Vol 38 (3) ◽  
pp. 228-244 ◽  
Author(s):  
Nenggen Ding ◽  
Saied Taheri

Abstract Easy-to-use tire models for vehicle dynamics have been persistently studied for such applications as control design and model-based on-line estimation. This paper proposes a modified combined-slip tire model based on Dugoff tire. The proposed model takes emphasis on less time consumption for calculation and uses a minimum set of parameters to express tire forces. Modification of Dugoff tire model is made on two aspects: one is taking different tire/road friction coefficients for different magnitudes of slip and the other is employing the concept of friction ellipse. The proposed model is evaluated by comparison with the LuGre tire model. Although there are some discrepancies between the two models, the proposed combined-slip model is generally acceptable due to its simplicity and easiness to use. Extracting parameters from the coefficients of a Magic Formula tire model based on measured tire data, the proposed model is further evaluated by conducting a double lane change maneuver, and simulation results show that the trajectory using the proposed tire model is closer to that using the Magic Formula tire model than Dugoff tire model.


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