APPLICATION OF FMEA-FTA IN RELIABILITY-CENTERED MAINTENANCE PLANNING

2012 ◽  
Vol 15 (15) ◽  
pp. 1-11
Author(s):  
I. Abdel-Aziz ◽  
M. Helal
Keyword(s):  
Maintenance Planning ◽  
Reliability Centered Maintenance

scholarly journals A Multicriteria Decision Model for Assessment of Failure Consequences in the RCM Approach

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Marcelo H. Alencar ◽  
Adiel T. de Almeida
Keyword(s):  
Failure Mode ◽  
Decision Model ◽  
Probability Distributions ◽  
Maintenance Planning ◽  
Multiattribute Utility ◽  
Five Dimensions ◽  
Multicriteria Decision ◽  
Reliability Centered Maintenance ◽  
Trade Offs ◽  
And Behavior

This paper proposes a multicriteria decision model based on MAUT (Multiattribute Utility Theory) incorporated into an RCM (Reliability Centered Maintenance) approach in order to provide a better assessment of the consequences of failure, allowing a more effective maintenance planning. MAUT provides an evaluation of probability distributions on each attribute as well as trade-offs involving lotteries. The model proposed takes advantage of such evaluations and it also restructures consequence groups established in an RCM approach into new five dimensions. As a result, overall indices of utility are computed for each failure mode analyzed. With these values, the ranking of the alternatives is established. The decision-maker’s preferences are taken into account so that the final result for each failure mode incorporates subjective aspects based on the decision-maker’s perceptions and behavior.

USCGC Healy (WAGB 20)—A Case Study for Implementing Reliability-Centered Maintenance

2000 ◽  
Vol 37 (01) ◽  
pp. 50-56
Author(s):  
William J. Reicks ◽  
Richard Burt ◽  
John P. Mazurana ◽  
Russell J. Steinle
Keyword(s):  
Planning Process ◽  
Technical Information ◽  
Cost Effective ◽  
Lessons Learned ◽  
Coast Guard ◽  
Fine Tuning ◽  
Maintenance Planning ◽  
Detail Design ◽  
Reliability Centered Maintenance

In new ship construction, maintenance planning affords both an opportunity and a challenge. On one hand, a new ship class enables maintenance planners to start with a clean slate and consider improved and more cost-effective maintenance methods. On the other hand, new manning concepts, lack of timely technical information when maintenance planning is conducted in parallel with detail design, use of equipment new to the fleet, and the like impose a measure of uncertainty on the planning process. In this paper, we review why and how Reliability-Centered Maintenance (RCM) techniques were applied to the new Polar icebreaker U.S. Coast Guard Cutter (CGC) Healy (frontispiece). We review how we incorporated condition-based maintenance techniques where appropriate. We discuss the decision process used for fine-tuning the Maintenance Procedure Cards (MPC) for CGC Healy's hull, mechanical, and electrical (HM&E) Preventive Maintenance Manual. Finally, we share some lessons learned in the process.

scholarly journals Integrating Production and Maintenance Planning as an Element of Success at the Tactical Level: A Fuzzy Control Theory Approach

2018 ◽  
Vol 26 (42) ◽  
pp. 109-117
Author(s):  
Ronald Díaz Cazañas ◽  
Daynier Rolando Delgado Sobrino ◽  
Estrella María De La Paz Martínez ◽  
Peter Košťál ◽  
Andrea Mudriková
Keyword(s):  
Fuzzy Control ◽  
Production Plan ◽  
Theory Approach ◽  
Maintenance Planning ◽  
Value Analysis ◽  
Reliability Centered Maintenance ◽  
Potential Capacity ◽  
Fixed Assets ◽  
System 2 ◽  
Definition Of

Abstract This contribution focuses on the topic of the Production–Maintenance integration at the tactical level and what it presents to the correct functioning and success of companies. For these purposes, a general framework for the integration of production and maintenance planning at the tactical level is introduced and described within this paper. It considers the key elements from the Value Analysis, Fuzzy Control and the Reliability-Centered Maintenance theories and implements them into the context of an existing method for selecting maintenance strategies. The proposal mainly focuses and addresses the definition of 1/ the roles played by the fixed assets that do really add value to the production system, 2/ the best maintenance strategy for each asset, and also addresses 3/ the modifications that, may be needed in the tactical production plan due to potential capacity problems in the maintenance subsystem.

scholarly journals Reliability Centered Maintenance to Determine Priority of Machine Damage Mode

2019 ◽  
Vol 125 ◽  
pp. 22005 ◽  
Author(s):  
Indah Rizkya ◽  
Ikhsan Siregar ◽  
Khawarita Siregar ◽  
Rahim Matondang ◽  
Enrico Waldo Henri
Keyword(s):  
Production Process ◽  
Preventive Maintenance ◽  
Positive Impact ◽  
Maintenance Planning ◽  
Damage Mode ◽  
Risk Priority Number ◽  
Damage Level ◽  
Reliability Centered Maintenance ◽  
Maintenance Activities ◽  
Fmea Method

High machine downtime results in the production process being disrupted. The average engine breakdown in the sterilizer production is 35.66 hours/month. The corrective maintenance method has not been able to guarantee a smooth production process. This study will plan preventive maintenance activities in order to increase the reliability of production machinery and also maintain the smooth production process. The study was carried out by identifying the damage level of a sterilizer machine with the FMEA method. Based on the result of the FMEA method, maintenance planning of the production machine using the Reliability Centered Maintenance (RCM). FMEA indicates that there are 5 highest risk priority number values, namely the IGBT, Mosfet, bearing bushings, rool former and v-block components. For the maintenance planning using the RCM method, the results obtained were the Total Minimum Downtime (TMD) criteria indicates that the optimum turnover interval of Roll Former, Mosfet, V-Block, IGBT, and Bearing Bushing components is 24 days, 23 days, 25 days, 26 days, and 22 days. The recommendation of the RCM maintenance method has the potential to have a positive impact, namely a decrease in the average critical component downtime of 37,103 % and also an increase in reliability.

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