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Research On Equipment Maintenance Management System Of Tunnel Boring Machine

Posted on:2019-07-13Degree:MasterType:Thesis
Country:ChinaCandidate:Z M LiFull Text:PDF
GTID:2382330563990179Subject:Mechanical engineering
Abstract/Summary:PDF Full Text Request
Tunnel Boring Machine plays an important role in the construction of railway,highway,water conservancy and city subway.In the process of construction,maintenance management of equipment can ensure the well driven condition of TBM.But,in the construction site universal maintenance records are lost and difficult to sense performance status and low efficiency by records and the accountability system is unclear.Lack of effective method of statistical analysis.Due to the lack of spare parts,it is difficult to control effectively for sudden accident and resulted in long downtime.For the above the construction in the maintenance of TBM,this paper designs a set of equipment maintenance management system.to archive technical data,spot check,maintenance,lubrication,operation state and trend prediction,spare parts and so on,change the passive situation,improve efficiency,reduce cost and ensure the safety of tunnel construction.In this paper,TBM structure composition is analyzed and the maintenance strategy and selection basis of TBM equipment are studied,in order to determine the detailed maintenance project of each equipment.In view of the image of the LabVIEW graphical programme on intuitive and convenient to use the software as platform and the system architecture and modules and the level of each module project is analyzed and designed.Put up a time series analysis methods module for Python and LabVIEW database operation program to stored and edited data.This paper has studied regression analysis and time series analysis state trend prediction of equipment for the maintenance.Which included equipment file management,spot check,maintenance,equipment lubrication,equipment status reminder,equipment spare parts and other modules.It modules can realize the functions of added,deleted,queried,report printed,equipment status prediction,and spare parts management optimization.
Keywords/Search Tags:TBM, maintenance, trend prediction, Python, LabVIEW
PDF Full Text Request
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