scholarly journals Developing a Decision Making Grid for determining proactive maintenance tactics

2018 ◽  
Vol 29 (8) ◽  
pp. 1296-1315 ◽  
Author(s):  
Arash Shahin ◽  
Nahid Aminsabouri ◽  
Kamran Kianfar
Keyword(s):  
Decision Making ◽  
Maintenance Policy ◽  
Content Type ◽  
Mean Time Between Failures ◽  
Time Between Failures ◽  
Reliability Centered Maintenance ◽  
Innovative Solution ◽  
Maintenance Policies ◽  
Mean Time ◽  
Proactive Maintenance

Purpose The purpose of this paper is to further develop the Decision Making Grid (DMG) proposed by Ashraf Labib (e.g. Labib, 1998, 2004; Fernandez et al., 2003; Aslam-Zainudeen and Labib, 2011; Stephen and Labib, 2018; Seecharan et al., 2018) by proposing an innovative solution for determining proactive maintenance tactics based on mean time between failures (MTBF) and mean time to repair (MTTR) indicators. Design/methodology/approach First, the influence of MTTR and MTBF indicators on proactive maintenance tactics was computed. The tactics included risk-based maintenance (RBM), reliability-centered maintenance (RCM), total productive maintenance (TPM), design out maintenance (DOM), accessibility-centered maintenance (ACM) and business-centered maintenance (BCM). Then, the tactics were allocated to the cells of a DMG with MTTR and MTBF axes. The proposed approach was examined on 32 pieces of equipment of the Esfahan Steel Company and appropriate maintenance tactics were consequently determined. Findings The findings indicate that the DOM, BCM, RBM and ACM tactics with weights of 0.86, 0.94, 0.68 and 1.00 are located at the corners of the DMG, respectively. The two remaining tactics of TPM and RCM are located at the middle corners. Also, the results indicate that the share of tactics per spotted equipment in the grid as 62, 22 and 16 percent for RCM, DOM and BCM, respectively. Research limitations/implications While reactive and preventive maintenance strategies include corrective, prospective, predetermined, proactive and predictive policies, the focus of this study was merely on the tactics of proactive maintenance policy. The advantage of the developed DMG over Labib’s DMG lies in its application for equipment with the unique condition of the bathtub curve. Originality/value While the basic DMG has been mostly used regardless of the type of maintenance policies, this study provides a DMG for a specific application regarding the proactive policy. In addition, the heuristic approach proposed for the development of DMG distinguishes this study from other studies.

A warranty based bilateral multi-issue negotiation approach

2015 ◽  
Vol 22 (7) ◽  
pp. 1247-1280 ◽  
Author(s):  
Prashant M. Ambad ◽  
Makarand S. Kulkarni
Keyword(s):  
Real Life ◽  
Spare Parts ◽  
Negotiation Process ◽  
Design Stage ◽  
Automated Negotiation ◽  
Content Type ◽  
Mean Time Between Failures ◽  
Time Between Failures ◽  
Target Values ◽  
Mean Time

Purpose – The purpose of this paper is to propose a warranty-based bilateral automated multi-issue negotiation approach. Design/methodology/approach – A methodology for bilateral automated negotiation process is developed considering the targets such as warranty attractiveness, warranty cost, mean time between failures, spare parts cost to the end user over the useful life of the life. The negotiation methodology is explained using different cases of negotiation. The optimization for each negotiation step is carried out using genetic algorithm with elitism strategy. Findings – The result after optimization indicates that the desired target values are achieved and manufacturer obtained desired profit margin. Practical implications – Application of automated negotiation model is illustrated using a real life case of an automobile engine manufacturer. The proposed approach helps the manufacturer of any product to develop a methodology for carrying out the negotiation process. The approach also results into taking warranty-related decisions at the design stage. Originality/value – This paper contributes in proposing a generalized methodology for warranty-based negotiation in which the negotiation is carried out between the manufacturer and the customer.

scholarly journals Life Cycle Cost versus Life Cycle Investment – A new Approach

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Life Cycle ◽  
Life Cycle Cost ◽  
Economic Life ◽  
Maintenance Policy ◽  
Mean Time Between Failures ◽  
Time Between Failures ◽  
The Mean ◽  
Physical Assets ◽  
Physical Asset ◽  
Mean Time

The paper proposes a model for the life cycle of physical assets that includes the maintenance policy, because it has direct implications on the equipment’s Return On Investment (ROI) and Life Cycle Cost; the developed model can be applied to any type of physical asset. The model is called Life Cycle Investment (LCI) instead of the traditional Life Cycle Cost (LCC). The paper proposes a new methodology based on the modified economic life cycle and lifespan methods by including the maintenance policy using maintenance Key Performance Indicators (KPI), namely Availability, based on the Mean Time Between Failures (MTBF) and the Mean Time To Repair (MTTR). The benefits (profits) that result from the asset’s Availability must be balanced with the initial investment and the variable maintenance investment along its life, which has relation with the maintenance policy and the ROI.

scholarly journals ANALYSIS OF INDICATORS AND COST OF WORLD CLASS MAINTENANCE (WCM) IN FOREST MACHINES

FLORESTA ◽  
2019 ◽  
Vol 49 (3) ◽  
pp. 533
Author(s):  
Carlos Cezar Cavassin Diniz ◽  
Eduardo Da Silva Lopes ◽  
Gabriel De Magalhães Miranda ◽  
Henrique Soares Koehler ◽  
Eduardo Kremer Custodio de Souza
Keyword(s):  
Maintenance Costs ◽  
Mean Time Between Failures ◽  
Time Between Failures ◽  
World Class ◽  
The Mean ◽  
Mean Time To Repair ◽  
Mean Time ◽  
Proactive Maintenance

The study was carried out at a forest company located in the Paraná State, Brazil, with the feller buncher, skidder and harvester. The following indicators were evaluated: mechanical availability, mean time between failures, mean time to repair, proactive maintenance index and maintenance costs, based on data obtained over a period of 18 months, contemplating the stages of implantation, maturation and stabilization of the WCM. The results showed an increase in the mechanical availability of the cutting and skidding machines from the implantation stage. The mean time between failures increased from the implantation stage, from 31.59 hours to 37.01 hours in the stabilization stage. As for the mean time to repairs, skidder and harvester presented an increase of 25.9% and 18.9% respectively; however, this increase in time represented an improvement in the quality of maintenance services, reflected in the results of mean time between failures. There was also a 31% increase in the proactive index of the machines studied, resulting in 9% reduction in maintenance costs between the deployment and stabilization stages.

Review of Technological Advancements and HSE-Based Safety Model for Deep-Water Human Occupied Vehicles

2014 ◽  
Vol 48 (3) ◽  
pp. 25-42 ◽  
Author(s):  
Narayanaswamy Vedachalam ◽  
Gidugu Ananada Ramadass ◽  
Malayath Aravindakshan Atmanand
Keyword(s):  
Deep Water ◽  
Capital Expenditure ◽  
Mean Time Between Failures ◽  
Safety Integrity Level ◽  
Geological Surveys ◽  
Time Between Failures ◽  
High Resolution Bathymetry ◽  
Safety Systems ◽  
Mean Time ◽  
Safety And Environment

AbstractThis paper reviews the latest advancements in subsea technologies associated with the safety of deep-water human occupied vehicles. Human occupied submersible operations are required for deep-water activities, such as high-resolution bathymetry, biological and geological surveys, search activities, salvage operations, and engineering support for underwater operations. As this involves direct human presence, the system has to be extremely safe and reliable. Based on applicable IEC 61508 Standards for health, safety, and environment (HSE), the safety integrity level requirements for the submersible safety systems are estimated. Safety analyses are done on 10 critical submersible safety systems with the assumption that the submersible is utilized for 10 deep-water missions per year. The results of the analyses are compared with the estimated target HSE requirements, and it is found that, with the present technological maturity and safety-centered design, it is possible to meet the required safety integrity levels. By proper maintenance, it is possible to keep the mean time between failures to more than 9 years. The results presented shall serve as a model for designers to arrive at the required trade-off between the capital expenditure, operating expenditure, and required safety levels.

Joint optimal production planning and proactive maintenance policy for a system subject to degradation

2019 ◽  
Vol 25 (2) ◽  
pp. 236-252 ◽  
Author(s):  
Lin Wang ◽  
Zhiqiang Lu ◽  
Xiaole Han
Keyword(s):  
Production Planning ◽  
Production System ◽  
Planning Horizon ◽  
Joint Model ◽  
Joint Production ◽  
Periodic Review ◽  
Maintenance Policy ◽  
Batch Production ◽  
Content Type ◽  
Proactive Maintenance

Purpose This paper integrates condition-based maintenance (CBM) with production planning in a single-stage production system that deteriorates with usage during a specified finite planning horizon. The purpose of this paper is to develop an integrated production and maintenance model to minimize the expected total cost over the horizon. Design/methodology/approach A joint production planning and CBM model is proposed. In the model, a set of products must be produced in lots. The system degradation is a stationary gamma process and the degradation level is detected by inspection between production lots. Maintenance actions including imperfect preventive maintenance (PM) should be taken when the failure risk exceeds the maintenance threshold. A fix-iterative heuristic algorithm is proposed to address the joint model. Findings The proactive policy expressed as a prognosis maintenance threshold is introduced to integrate CBM with batch production perfectly. Experiments are carried out to conduct sensitivity analysis, which provides some insights to facilitate industrial manufacturing. The superiority of the proposed joint model compared with a separate decision method is demonstrated. The results show an advantage in cost saving. Originality/value Few studies have been made to integrate production planning and CBM decisions, especially for a multi-product system. Their maintenance decisions are usually based on a periodic review policy, which is not appropriate for batch production system. A prognosis maintenance threshold based on system condition and production quantity is suitable for the integrated decisions. Moreover, the imperfect PM is taken into consideration in this paper. A fix-iterative algorithm is developed to solve the joint model. This work forms a proactive maintenance for batch production.

scholarly journals A Statistical State Analysis of a Marine Gas Turbine

Actuators ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 54 ◽  
Author(s):  
Suzana Lampreia ◽  
Valter Vairinhos ◽  
Victor Lobo ◽  
José Requeijo
Keyword(s):  
Gas Turbine ◽  
Point Of View ◽  
Operating Conditions ◽  
Environmental Data ◽  
Statistical Point ◽  
Mean Time Between Failures ◽  
Time Between Failures ◽  
Intervention Plan ◽  
Mean Time

This paper describes the analysis, from a statistical point of view, of a maritime gas turbine, under various operating conditions, so as to determine its state. The data used concerns several functioning parameters of the turbines, such as temperatures and vibrations, environmental data, such as surrounding temperature, and past failures or quasi-failures of the equipment. The determination of the Mean Time Between Failures (MTBF) gives a rough estimate of the state of the turbine, but in this paper we show that it can be greatly improved with graphical and statistical analysis of data measured during operation. We apply the Laplace Test and calculate the gas turbine reliability using that data, to define the gas turbine failure tendency. Using these techniques, we can have a better estimate of the turbine’s state, and design a preventive observation, inspection and intervention plan.

On preventive maintenance policies: a selection framework

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Imad Alsyouf ◽  
Sadeque Hamdan ◽  
Mohammad Shamsuzzaman ◽  
Salah Haridy ◽  
Iyad Alawaysheh
Keyword(s):  
Preventive Maintenance ◽  
Fuzzy Ahp ◽  
Critical Component ◽  
Multi Objective Optimization ◽  
Maintenance Policy ◽  
Coolant Pump ◽  
Content Type ◽  
Multi Objective ◽  
Maintenance Policies ◽  
Preventive Maintenance Policy

PurposeThis paper develops a framework for selecting the most efficient and effective preventive maintenance policy using multiple-criteria decision making and multi-objective optimization.Design/methodology/approachThe critical component is identified with a list of maintenance policies, and then its failure data are collected and the optimization objective functions are defined. Fuzzy AHP is used to prioritize each objective based on the experts' questionnaire. Weighted comprehensive criterion method is used to solve the multi-objective models for each policy. Finally, the effectiveness and efficiency are calculated to select the best maintenance policy.FindingsFor a fleet of buses in hot climate environment where coolant pump is identified as the most critical component, it was found that block-GAN policy is the most efficient and effective one with a 10.24% of cost saving and 0.34 expected number of failures per cycle compared to age policy and block-BAO policy.Research limitations/implicationsOnly three maintenance policies are compared and studied. Other maintenance policies can also be considered in future.Practical implicationsThe proposed methodology is implemented in UAE for selecting a maintenance scheme for a critical component in a fleet of buses. It can be validated later in other Gulf countries.Originality/valueThis research lays a solid foundation for selecting the most efficient and effective preventive maintenance policy for different applications and sectors using MCDM and multi-objective optimization to improve reliability and avoid economic loss.

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