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Hasel Amini khoshalan

Hasel Amini khoshalan

Academic rank: Assistant Professor
ORCID:
Education: PhD.
ScopusId: 1111111
HIndex:
Faculty: Faculty of Engineering
Address:
Phone: 08733660073

Research

Title
RAM analysis of earth pressure balance tunnel boring machines: A case study
Type
JournalPaper
Keywords
availability, maintainability, reliability, tunnel boring machine
Year
2014
Journal international journal of mining and geo-engineering
DOI
Researchers Hasel Amini khoshalan ، Seyed Rahman Torabi ، Seyed Hadi Hoseinie ، Behzad Ghodrati

Abstract

Earth pressure balance tunnel boring machines (EPB-TBMs) are favorably applied in urban tunneling projects. Despite their numerous advantages, considerable delays and high maintenance cost are the main disadvantages these machines suffer from. Reliability, availability, and maintainability (RAM) analysis is a practical technique that uses failure and repair dataset obtained over a reasonable time for dealing with proper machine operation, maintenance scheduling, cost control, and improving the availability and performance of such machines. In the present study, a database of failures and repairs of an EBP-TBM was collected in line 1 of Tabriz subway project over a 26-month interval of machine operation. In order to model the reliability of the TBM, this machine was divided into five distinct subsystems including mechanical, electrical, hydraulic, pneumatic, and water systems in a series configuration. According to trend and serial correlation tests, the renewal processes were applied, for analysis of all subsystems. After calculating the reliability and maintainability functions for all subsystems, it was revealed that the mechanical subsystem with the highest failure frequency has the lowest reliability and maintainability. Similarly, estimating the availability of all subsystems indicated that the mechanical subsystem has a relatively low availability level of 52.6%, while other subsystems have acceptable availability level of 97%. Finally, the overall availability of studied machine was calculated as 48.3%.