scholarly journals RAILWAY CONDITION-BASED MAINTENANCE MODEL WITH STOCHASTIC DETERIORATION

2014 ◽  
Vol 20 (5) ◽  
pp. 686-692 ◽  
Author(s):  
Cecília Vale ◽  
Isabel M. Ribeiro
Keyword(s):  
Standard Deviation ◽  
Stochastic Simulation ◽  
Preventive Maintenance ◽  
Real Data ◽  
Condition Based Maintenance ◽  
Nonlinear Program ◽  
Monte Carlo Techniques ◽  
Deterioration Rate ◽  
Maintenance Model ◽  
Over Time

The application of mathematical programming for scheduling preventive maintenance in railways is relatively new. This paper presents a stochastic mathematical model designed to optimize and to predict tamping operations in ballasted tracks as preventive condition-based maintenance. The model is formulated as a mixed 0–1 nonlinear program that considers real technical aspects as constraints: the reduction of the geometrical track quality over time is characterized by the deterioration rate of the standard deviation of the longitudinal level; the track layout; the dependency of the track recovery on its quality at the moment of the maintenance operation; the limits for preventive maintenance that depend on the maximum permissible train speed. In the model application, a railway stretch with 51.2 km of length is analysed for a time period of five years. The deterioration model is stochastic and represents the reduction of the standard deviation of the longitudinal level over time. The deterioration rate of the standard deviation of the longitudinal level is simulated by Monte Carlo techniques, considering the three parameters Dagum probabilistic distribution fitted with real data (Vale, Simões 2012). Two simulations are performed and compared: stochastic simulation in space; stochastic simulation in space and time. The proposed condition-based maintenance model is able to produce optimal schedules within appropriate computational times.

A Dynamic Reliability-Centered Maintenance Analysis Method for Natural Gas Compressor Station Based on Diagnostic and Prognostic Technology

2015 ◽  
Vol 138 (6) ◽  
Author(s):  
Dengji Zhou ◽  
Huisheng Zhang ◽  
Yi-Guang Li ◽  
Shilie Weng
Keyword(s):  
Natural Gas ◽  
Preventive Maintenance ◽  
Condition Based Maintenance ◽  
Equipment Management ◽  
Analysis Method ◽  
Dynamic Reliability ◽  
Maintenance Schedule ◽  
Reliability Centered Maintenance ◽  
Maintenance Decision ◽  
Maintenance Model

The availability requirement of natural gas compressors is high. Thus, current maintenance architecture, combined periodical maintenance and simple condition based maintenance, should be improved. In this paper, a new maintenance method, dynamic reliability-centered maintenance (DRCM), is proposed for equipment management. It aims at expanding the application of reliability-centered maintenance (RCM) in maintenance schedule making to preventive maintenance decision-making online and seems suitable for maintenance of natural gas compressor stations. A decision diagram and a maintenance model are developed for DRCM. Then, three application cases of DRCM for actual natural gas compressor stations are shown to validate this new method.

Study on Information System with Condition-Based Maintenance Models for a Continuously Degrading System

2013 ◽  
Vol 703 ◽  
pp. 227-230 ◽  
Author(s):  
Ao Lin Huang ◽  
Qing Min Li ◽  
Tie Bing Li
Keyword(s):  
Information System ◽  
Preventive Maintenance ◽  
Process Models ◽  
Condition Based Maintenance ◽  
Optimal Solutions ◽  
Model And Simulation ◽  
Maintenance Time ◽  
Degrading System ◽  
Maintenance Model

This paper considers on-condition maintenance of a continuously degrading information system, in which the inter-maintenance time and the maintenance time depend on the condition of the system at which maintenance is carried out. The durations of preventive maintenance activities are supposed to be exponentially distributed. Assuming deterioration of the system follows a gamma process, models are established to maximize the system average availability when the effectiveness of the preventive maintenance activity becomes weaker and weaker. Optimal solutions on the condition of the system at which maintenance should be performed and the number of times of maintenance action to be carried out are obtained based on Monte-Carlo simulation. A case study is given to show the procedure of the maintenance model and simulation.

scholarly journals Preventive Maintenance Model for National School Buildings in Indonesia Using a Constraint Programming Approach

2021 ◽  
Vol 13 (4) ◽  
pp. 1874
Author(s):  
Shu-Shun Liu ◽  
Muhammad Faizal Ardhiansyah Arifin
Keyword(s):  
Constraint Programming ◽  
Preventive Maintenance ◽  
Maintenance Cost ◽  
Programming Approach ◽  
School Buildings ◽  
Maintenance Planning ◽  
Building Conditions ◽  
Building Maintenance ◽  
Deterioration Rate ◽  
Maintenance Model

The Indonesian government needs to maintain around 231,000 school buildings in active use. Such a portfolio of buildings given the diversity of locations, limited maintenance budget, and deterioration rates varied by different building conditions presents many challenges to effective maintenance planning. Many of those schools had been reported to be aging and in a degenerated condition. However, contemporary practice for the planning method of Indonesia’s building maintenance program applies reactive maintenance strategies with a single linear deterioration rate. Such methodology cannot properly guarantee the sustainability of those school buildings. Therefore, this study attempts to examine a different approach to Indonesia’s building maintenance planning by adopting a preventive maintenance strategy using the deterioration rate model proved by historical data from a previous study. This study develops an optimization model with varied deterioration rates and considers the budget limitation, by utilizing a Constraint Programming (CP) approach. The proposed model achieves the minimum maintenance cost for a real case of 41 school buildings under different deterioration rates to ensure adequate building conditions and maintain expected levels of service. Finally, research analysis also proves that this new preventive maintenance model has potential to deliver superior capability for assisting building maintenance decisions in Indonesia’s government.

A Dynamic RCM Analysis Method for Natural Gas Compressor Station Based on Diagnostic and Prognostic Technology

2015 ◽  
Author(s):  
Dengji Zhou ◽  
Huisheng Zhang ◽  
Yi-Guang Li ◽  
Shilie Weng
Keyword(s):  
Natural Gas ◽  
Preventive Maintenance ◽  
Condition Based Maintenance ◽  
Equipment Management ◽  
Analysis Method ◽  
Dynamic Reliability ◽  
Maintenance Schedule ◽  
Reliability Centered Maintenance ◽  
Maintenance Decision ◽  
Maintenance Model

The availability requirement of natural gas compressors is high. Thus, current maintenance architecture, combined periodical maintenance and simple condition based maintenance, should be improved. In this paper, a new maintenance method, Dynamic Reliability-centered Maintenance (DRCM), is proposed for equipment management. It aims at expanding the application of Reliability-centered Maintenance (RCM) in maintenance schedule making to preventive maintenance decision making online and seems suitable for maintenance of natural gas compressor stations. A decision diagram and a maintenance model are developed for DRCM. Then three application cases of DRCM for actual natural gas compressor stations are shown to validate this new method.

scholarly journals Transition Activity Recognition System Based on Standard Deviation Trend Analysis

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3117
Author(s):  
Junhao Shi ◽  
Decheng Zuo ◽  
Zhan Zhang
Keyword(s):  
Standard Deviation ◽  
Trend Analysis ◽  
Activity Recognition ◽  
Human Activity ◽  
Real Data ◽  
Recognition System ◽  
Electromechanical Systems ◽  
Transition Activities ◽  
Overall Performance ◽  
Transition Activity

With the development and popularity of micro-electromechanical systems (MEMS) and smartphones, sensor-based human activity recognition (HAR) has been widely applied. Although various kinds of HAR systems have achieved outstanding results, there are still issues to be solved in this field, such as transition activities, which means the transitional process between two different basic activities, discussed in this paper. In this paper, we design an algorithm based on standard deviation trend analysis (STD-TA) for recognizing transition activity. Compared with other methods, which directly take them as basic activities, our method achieves a better overall performance: the accuracy is over 80% on real data.

scholarly journals Inferences for Weibull parameters under progressively first-failure censored data with binomial random removals

2020 ◽  
Vol 9 (1) ◽  
pp. 47-60
Author(s):  
Samir K. Ashour ◽  
Ahmed A. El-Sheikh ◽  
Ahmed Elshahhat
Keyword(s):  
Monte Carlo ◽  
Censored Data ◽  
Real Data ◽  
Unknown Parameters ◽  
Data Set ◽  
Monte Carlo Simulation Study ◽  
Squared Error Loss Function ◽  
Bayes Estimates ◽  
Monte Carlo Techniques ◽  
Credible Intervals

In this paper, the Bayesian and non-Bayesian estimation of a two-parameter Weibull lifetime model in presence of progressive first-failure censored data with binomial random removals are considered. Based on the s-normal approximation to the asymptotic distribution of maximum likelihood estimators, two-sided approximate confidence intervals for the unknown parameters are constructed. Using gamma conjugate priors, several Bayes estimates and associated credible intervals are obtained relative to the squared error loss function. Proposed estimators cannot be expressed in closed forms and can be evaluated numerically by some suitable iterative procedure. A Bayesian approach is developed using Markov chain Monte Carlo techniques to generate samples from the posterior distributions and in turn computing the Bayes estimates and associated credible intervals. To analyze the performance of the proposed estimators, a Monte Carlo simulation study is conducted. Finally, a real data set is discussed for illustration purposes.

Sign in / Sign up

Export Citation Format

Share Document