Optimum Maintenance And Turnover Scheduling For Locomotives In Electrified Railway

Reasonable maintenance and scheduling of electrified railway locomotives is an important task to ensure the safety and operational efficiency of railway transportation.The research on the appropriate preventive maintenance for electrical locomotives is carried out in this paper due to insufficient or excessive maintenance.This paper summarises the existing research on the preventive maintenance of electric locomotives and formulates a new maintenance scheduling which is expected to be used for locomotive turnover scheduling and achieve an integration of maintenance and turnover scheduling to ensure the high efficient operation of locomotives.The main work of the thesis includes the following aspects.Firstly,the probability of defect inspection and the maintenance effect are not perfect in the actual maintenance work,these influence factors are added to the basic delay time theory in this paper,and maintenance scheduling optimization model for single component based on delay time theory is proposed aiming at the minimum expected downtime expense in unit time.A numerical simulation algorithm is developed toattain the optimal maintenance cycle.An example is given to validate the parameter estimation proposed in this paper and obtain the optimum maintenance cycle of the given component.The results from the sensitivity test show that the probability of defect inspection and the effect due to each maintenance activity,i.e.age reduction factor will affect the optimization results.The smaller the service age reduction factor is,the smaller the expected downtime expense is;while the greater the probability of defect inspection is,the smaller the expected cost downtime expense is.Secondly,a Bayesian network model is established to analyze the reliability relationship among the locomotive system,its sub-systems and components.Based on the opportunity maintenance theory,the multi-component opportunity preventive maintenance model for locomotives based on Bayesian network is proposed.A genetic algorithm is developed to solve the proposed model.The opportunity maintenance strategy of SS4B electric locomotive air pipeline subsystem is analyzed in case studies.The results show that under the constrain of system reliability based on Bayesian network,the opportunity maintenance scheduling can not only ensure the reliability of the system simultaneously,but also adjust the maintenance time of components in advance or delay to achieve merge mainteance,the latter reduce the number of maintenance and the total downtime cost.Finally,in the optimization model of locomotives scheduling plan,the multi-component opportunity maintenance plan is added to the model as a constraint,and the reliability of connection services provided by adjacent trains is put forward to quantify the the risk of connection,which is one of the optimum objectives.This paper estalishes an optimization model of the locomotive scheduling plan based on the multi-component opportunity maintenance scheduling and considering the reliability of the train connection,and designs the improved ant colony algorithm to solve the model.A case study shows that the multi-component opportunity maintenance can reduce the length of a single maintenance activity and increase the flexibility of locomotive scheduling plan.In addition,a highly reliable circulation for locomotive operation through an appropriate increment of their connecting time is helpful to reduce the high risk from the invalidation of train operational connects.