scholarly journals Overview of the Intelligent Operation and Maintenance System for Shanghai Rail transit Rolling Stock

New Metro ◽  
2020 ◽  
Vol 1 (1) ◽  
pp. 22-27
Author(s):  
Tu Xiaowei ◽  
Yu Youmin ◽  
Deng Qi
Keyword(s):  
Online Monitoring ◽  
Rolling Stock ◽  
Rail Transit ◽  
Work Related ◽  
Maintenance System ◽  
Model Based ◽  
Operation And Maintenance ◽  
Maintenance Model

The traditional rolling stock maintenance model based on planned repairs and fault repairs cannot meet the needs of the rolling stock operation and maintenance for superlarge-scaled networks. There is an urgent need to establish a highly automated and information-based rolling stock intelligent operation and maintenance system based on online monitoring. This paper introduces the composition, main functions and application situations for the Shanghai rail transit intelligent rolling stock operation and maintenance system as well as suggestions for further work related thereto so as to provide references for the development of the rolling stock intelligent operation and maintenance system of the rail transit industry.

Integrated Train Timetabling and Rolling Stock Scheduling Model Based on Time-Dependent Demand for Urban Rail Transit

2017 ◽  
Vol 32 (10) ◽  
pp. 856-873 ◽  
Author(s):  
Yixiang Yue ◽  
Juntao Han ◽  
Shifeng Wang ◽  
Xiang Liu
Keyword(s):  
Urban Rail Transit ◽  
Time Dependent ◽  
Rolling Stock ◽  
Rail Transit ◽  
Train Timetabling ◽  
Model Based ◽  
Scheduling Model ◽  
Urban Rail ◽  
Dependent Demand

scholarly journals Model of passenger rolling stock maintenance

2018 ◽  
Vol 216 ◽  
pp. 02018 ◽  
Author(s):  
Dmitry Bannikov ◽  
Nina Sirina
Keyword(s):  
High Speed ◽  
Discrete Event ◽  
Rolling Stock ◽  
Maintenance And Repair ◽  
Maintenance System ◽  
Repair Costs ◽  
Facilities Location ◽  
Event Model ◽  
Maintenance Model

In order to improve the quality of maintenance and repair of a passenger rolling stock, Russian Railways JSC requires transition to routine maintenance. The paper studies the main stages of building a discrete-event model of a passenger rolling stock maintenance using AnyLogic environment. The following key parameters of maintenance are used: maintenance facilities location, rolling stock operability index, probability index for operational efficiency (downtime and failure probability), maintenance and repair costs. The maintenance model is implemented in the integrated modeling library. Simulation model enables to implement the modeled system and obtain performance indicators of routine maintenance system operation. By simulating various conditions it is possible to evaluate efficiency of the studied maintenance system for a high-speed passenger rolling stock.

scholarly journals SOMS: A Subway Operation and Maintenance System Based on Planned Maintenance Model with Train State

2013 ◽  
Vol 05 (04) ◽  
pp. 195-202 ◽  
Author(s):  
Jianlong Ding ◽  
Yong Qin ◽  
Limin Jia ◽  
Shiyou Zhu ◽  
Bo Yu
Keyword(s):  
Maintenance System ◽  
Operation And Maintenance ◽  
Maintenance Model

A motorway lighting system maintenance model based on simulation

1983 ◽  
Vol 6 (2) ◽  
pp. 63-81 ◽  
Author(s):  
D.M. Barry ◽  
R.J.F. Garty
Keyword(s):  
Lighting System ◽  
Model Based ◽  
System Maintenance ◽  
Maintenance Model

Failure Risk Indicators for a Maintenance Model Based on Observable Life of Industrial Components With an Application to Wind Turbines

2013 ◽  
Vol 62 (3) ◽  
pp. 569-582 ◽  
Author(s):  
Rodrigo J. de Andrade Vieira ◽  
Miguel A. Sanz-Bobi
Keyword(s):  
Wind Turbines ◽  
Risk Indicators ◽  
Model Based ◽  
Failure Risk ◽  
Maintenance Model

Optimal Operation and Maintenance of Gas Compressor Stations: An Integrated Framework Applied to a Large-Scale Industrial Case

2015 ◽  
Vol 138 (4) ◽  
Author(s):  
Dionysios P. Xenos ◽  
Erling Lunde ◽  
Nina F. Thornhill
Keyword(s):  
Optimization Model ◽  
Large Scale ◽  
Optimal Schedule ◽  
Optimal Operation ◽  
Optimal Distribution ◽  
Compressor Station ◽  
Operational Costs ◽  
Operation And Maintenance ◽  
Maintenance Model ◽  
Operational Aspects

This paper presents a framework which integrates maintenance and optimal operation of multiple compressors. The outcome of this framework is a multiperiod plan which provides the schedule of the operation of compressors: the schedule gives the best decisions to be taken, for example, when to carry out maintenance, which compressors to use online and how much to load them. These decisions result in the minimization of the total operational costs of the compressors while at the same time the demand of the plant is met. The suggested framework is applied to an industrial gas compressor station which encompasses large multistage centrifugal compressors operating in parallel. The optimization model of the framework consists of three main parts: the models of compressor maps, the operational aspects of compressors, and a maintenance model. The results illustrate the optimal schedule for 90 days and an example of the optimal distribution of the load of the compressors for 5 days. Finally, the results show the economical benefits from the integration of maintenance and optimization.

scholarly journals Three Revised Kalman Filtering Models for Short-Term Rail Transit Passenger Flow Prediction

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Pengpeng Jiao ◽  
Ruimin Li ◽  
Tuo Sun ◽  
Zenghao Hou ◽  
Amir Ibrahim
Keyword(s):  
Real Time ◽  
Nonparametric Regression ◽  
Kalman Filtering ◽  
Rail Transit ◽  
Correction Coefficient ◽  
Short Term ◽  
Transit System ◽  
Filtering Method ◽  
Passenger Flow ◽  
Model Based

Short-term prediction of passenger flow is very important for the operation and management of a rail transit system. Based on the traditional Kalman filtering method, this paper puts forward three revised models for real-time passenger flow forecasting. First, the paper introduces the historical prediction error into the measurement equation and formulates a revised Kalman filtering model based on error correction coefficient (KF-ECC). Second, this paper employs the deviation between real-time passenger flow and corresponding historical data as state variable and presents a revised Kalman filtering model based on Historical Deviation (KF-HD). Third, the paper integrates nonparametric regression forecast into the traditional Kalman filtering method using a Bayesian combined technique and puts forward a revised Kalman filtering model based on Bayesian combination and nonparametric regression (KF-BCNR). A case study is implemented using statistical passenger flow data of rail transit line 13 in Beijing during a one-month period. The reported prediction results show that KF-ECC improves the applicability to historical trend, KF-HD achieves excellent accuracy and stability, and KF-BCNR yields the best performances. Comparisons among different periods further indicate that results during peak periods outperform those during nonpeak periods. All three revised models are accurate and stable enough for on-line predictions, especially during the peak periods.

A new imperfect maintenance model based on delay-time concepts for single components with multiple failure modes

2014 ◽  
Vol 6 (4) ◽  
pp. 479-486 ◽  
Author(s):  
Xiufeng Li ◽  
Renyang He ◽  
Zhiwei Yan ◽  
Haijun Hu ◽  
Guangxu Cheng
Keyword(s):  
Delay Time ◽  
Failure Modes ◽  
Imperfect Maintenance ◽  
Multiple Failure Modes ◽  
Model Based ◽  
Time Concepts ◽  
Maintenance Model
Sign in / Sign up

Export Citation Format

Share Document