Markov Process on Optimal Maintenance Strategy of Urban Rail Transit Equipment

Cao, Lei; Zhang, Danyu; Hu, Rui; Li, Weihua

doi:10.1142/9789813220362_0006

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yuan, et al [14][15][16] established the state degradation model of equipment under continuous state monitoring and periodic state monitoring based on the life distribution of train on-board equipment, and optimized the CBM strategy with the state threshold as the core under the constraint of component availability and maintenance cost. Chen [17] focused on the study of trouble-free time for various equipment used in urban rail transit, obtained its probability distribution model by a trial error method, and determined the interval of TBM by combining the relationship between reliability characteristics. Based on reliability theory and the PHM model, Luo [18] analyzed the relationship between historical fault data and historical monitoring data of wind turbine components including generator, gearbox, bearing system, etc., and designed the CBM strategy for repairable components of wind turbine.…”

Section: Introductionmentioning

confidence: 99%

Analysis of a State Degradation Model and Preventive Maintenance Strategies for Wind Turbine Generators Based on Stochastic Differential Equations

Zhao

Wang

2023

Mathematics

View full text Add to dashboard Cite

Preventive maintenance is widely used in wind turbine equipment to ensure their safe and reliable operation, and this mainly includes time-based maintenance (TBM) and condition-based maintenance (CBM). Most wind farms only use TBM as the main maintenance strategy in engineering practice. Although this can meet certain reliability requirements, it cannot fully utilize the characteristics of TBM and CBM. For this, a state model based on the stochastic differential equation (SDE) is established in this paper to describe the spatio-temporal evolution process of the degradation behavior of wind turbine generators, in which the components’ failure is represented by a proportional hazards model, the random fluctuation of the state is simulated by the Brownian motion, and the SDE model is solved by a function transformation method. Based on the model, the characteristics of TBM and CBM, and the asymptotic relationship between them, are discussed and analyzed, the necessity and feasibility of their combination are expounded, and a joint maintenance strategy is proposed and analyzed. The results show that the stochastic model can better reflect the real deterioration state of the generator. Moreover, TBM has a fixed maintenance interval, depending on global sample tracks and, only depending on the local sample track, CBM can follow the component state. Finally, the rationality and effectiveness of the proposed model and results are verified by a practical example.

show abstract

Section: Introductionmentioning

confidence: 99%