scholarly journals Maintenance Costs and Advanced Maintenance Techniques: Survey and Analysis

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Douglas S Thomas ◽  
Brian Weiss
Keyword(s):  
Monte Carlo ◽  
Preventive Maintenance ◽  
Monte Carlo Analysis ◽  
Predictive Maintenance ◽  
The Other ◽  
Maintenance Costs ◽  
Maintenance Techniques ◽  
Using Data

The costs/benefits associated with investing in advanced maintenance techniques is not well understood. Using data collected from manufacturers, we estimate the national losses due to inadequate maintenance and make comparisons between those that rely on reactive maintenance, preventive maintenance, and predictive maintenance. The total annual costs/losses associated with maintenance is estimated to be on average $222.0 billion, as estimated using Monte Carlo analysis. Respondents were categorized into three groups and compared. The first group is the top 50 % of respondents that rely on reactive maintenance, measured in expenditures. The remaining respondents were split in half based on their reliance on predictive maintenance. The top 50 % of respondents in using reactive maintenance, measured in expenditures, compared to the other respondents suggests that there are substantial benefits of moving away from reactive maintenance toward preventive and/or predictive maintenance. The bottom 50 %, which relies more heavily on predictive and preventive maintenance, had 52.7 % less unplanned downtime and 78.5 % less defects. The comparison between the smaller two groups, which rely more heavily on preventive and predictive maintenance, shows that there is 18.5 % less unplanned downtime and 87.3 % less defects for those that rely more on predictive than preventive.

Stochastic Analysis of Groundwater Traveltime for Long-Term Repository Performance Assessment

1983 ◽  
Vol 26 ◽  
Author(s):  
P.M. Clifton ◽  
R.G. Baca ◽  
R.C. Arnett
Keyword(s):  
Monte Carlo ◽  
Random Number Generator ◽  
Monte Carlo Analysis ◽  
Effective Porosity ◽  
Specific Storage ◽  
Hanford Site ◽  
Transient Problem ◽  
Degree Of Confidence ◽  
Using Data

ABSTRACTThis paper describes a method of stochastically analyzing groundwater traveltime. The method uses a Monte Carlo technique to generate a suite of random spatial fields that are subsequently input to the groundwater flow and groundwater traveltime equations. Stochastic inputs to these equations can be (1) transmissivity (or hydraulic conductivity), (2) effective thickness (or effective porosity), or (3) boundary conditions. In a transient problem, storage coefficient (or specific storage) could also be stochastically treated. Spatial correlation in the random input fields is accounted for by means of a multivariate random-number generator, which requires the first two statistical moments of these fields to be specified. The output from the Monte Carlo analysis is a suite of groundwater traveltime realizations that can be used to derive exceedance probabilities. These probabilities provide a measure of the degree of confidence in meeting set criteria.A preliminary application of this method using data from the deep basalts beneath the Hanford Site is also presented. The results illustrate how this method can be used to evaluate compliance with a technical criterion relating to groundwater traveltime.

Using Neural Network for Diagnostics of an Industrial Gas Turbine

2004 ◽  
Author(s):  
M. Ghoreyshi ◽  
R. Singh
Keyword(s):  
Neural Networks ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Industrial Applications ◽  
Predictive Maintenance ◽  
Training Data ◽  
Training Algorithms ◽  
Maintenance Costs ◽  
Maintenance Techniques ◽  
Industrial Gas Turbine

Gas Turbines are being utilized in increasing numbers for industrial applications because of their increasing in efficiency and reliability. However, they become degraded during operation and their associated maintenance costs can become extremely high for the owners. Hence, successful maintenance techniques are those which are able to reduce maintenance costs and down-time. In recent decades industry has started to use predictive maintenance techniques because of their benefits in reducing down-time compared to traditional techniques like breakdown maintenance, as a result different predictive maintenance and diagnostics techniques have been developed during the last fifteen years. This study, in particular, will focus on performance diagnostic techniques based on Neural Networks. The network features and training algorithms will be discussed to develop an appropriate model for gas turbine diagnostics. In addition, it will be shown how training data can affect training performance. This study follows on from previous work carried out at Cranfield University to develop engine health monitoring techniques; however it will attempt to investigate the different abilities of neural networks for use in industrial gas turbine diagnostics, especially in non-standard ambient temperatures and its advantages compared to Gas Path Analysis (GPA) techniques.

Generation of Correlated Logistic-Normal Random Variates for Medical Decision Trees

1998 ◽  
Vol 37 (03) ◽  
pp. 235-238 ◽  
Author(s):  
M. El-Taha ◽  
D. E. Clark
Keyword(s):  
Monte Carlo ◽  
Decision Trees ◽  
Computer Algebra ◽  
Taylor Series ◽  
Computer Algebra System ◽  
Random Variable ◽  
Monte Carlo Analysis ◽  
Normal Random Variable ◽  
Medical Decision ◽  
Theoretical Results

AbstractA Logistic-Normal random variable (Y) is obtained from a Normal random variable (X) by the relation Y = (ex)/(1 + ex). In Monte-Carlo analysis of decision trees, Logistic-Normal random variates may be used to model the branching probabilities. In some cases, the probabilities to be modeled may not be independent, and a method for generating correlated Logistic-Normal random variates would be useful. A technique for generating correlated Normal random variates has been previously described. Using Taylor Series approximations and the algebraic definitions of variance and covariance, we describe methods for estimating the means, variances, and covariances of Normal random variates which, after translation using the above formula, will result in Logistic-Normal random variates having approximately the desired means, variances, and covariances. Multiple simulations of the method using the Mathematica computer algebra system show satisfactory agreement with the theoretical results.

Monte Carlo Analysis of Nonequilibrium Ionized Flows.

1996 ◽  
Author(s):  
Iain D. Boyd ◽  
Xiaoming Liu ◽  
Jitendra Balakrishnan
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Analysis

Foreign Aid and its Role in Maintaining the Exploitation of the Agricultural Sector: Evidence from a Case Study in Africa

1983 ◽  
Vol 13 (1) ◽  
pp. 33-49 ◽  
Author(s):  
Claudio Schuftan
Keyword(s):  
Rural Development ◽  
Foreign Aid ◽  
Local Governments ◽  
Agricultural Sector ◽  
National Development ◽  
The Other ◽  
Sizable Proportion ◽  
Using Data ◽  
State Of Affairs

Today most foreign aid donors are genuinely committed to the idea that development in Third World countries should start with rural development. Therefore, a sizable proportion of their development funds are invested in rural projects. However, donors channel these funds through local governments (most often representing local bourgeois interests) that are not as committed to the principle of rural development. These governments are often also embarked in policies that are actually—directly or indirectly—expropriating the surpluses generated by agriculture and investing them in the other sectors of the economy. The peasants are therefore footing most of the bill of overall national development. This paper contends that, because of this state of affairs, foreign aid directed toward rural development is actually filling the investment gap left by an internal system of unequal returns to production in agriculture. In so doing, foreign aid is indirectly financing the development of the other sectors of the economy, even if this result is unintended. This perpetrates maldevelopment without redressing the basic exploitation process of peasants which lies at the core of underdevelopment. Evidence to support this hypothesis is presented using data from a primarily agricultural exporting country: the United Republic of Cameroon.

Sign in / Sign up

Export Citation Format

Share Document