scholarly journals A Simulation Model for Machine Efficiency Improvement Using Reliability Centered Maintenance: Case Study of Semiconductor Factory

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Srisawat Supsomboon ◽  
Kanthapong Hongthanapach
Keyword(s):  
Preventive Maintenance ◽  
Selection Process ◽  
Reliability Estimation ◽  
Maintenance Cost ◽  
Risk Level ◽  
Test Machine ◽  
Optimization Approach ◽  
Efficiency Improvement ◽  
Reliability Centered Maintenance ◽  
Maintenance Plan

The purpose of this study was to increase the quality of product by focusing on the machine efficiency improvement. The principle of the reliability centered maintenance (RCM) was applied to increase the machine reliability. The objective was to create preventive maintenance plan under reliability centered maintenance method and to reduce defects. The study target was set to reduce the Lead PPM for a test machine by simulating the proposed preventive maintenance plan. The simulation optimization approach based on evolutionary algorithms was employed for the preventive maintenance technique selection process to select the PM interval that gave the best total cost and Lead PPM values. The research methodology includes procedures such as following the priority of critical components in test machine, analyzing the damage and risk level by using Failure Mode and Effects Analysis (FMEA), calculating the suitable replacement period through reliability estimation, and optimizing the preventive maintenance plan. From the result of the study it is shown that the Lead PPM of test machine can be reduced. The cost of preventive maintenance, cost of good product, and cost of lost product were decreased.

An Application of Reliability Centered Maintenance Technique for Preventive Maintenance in Refinery Plant

2016 ◽  
Vol 848 ◽  
pp. 244-250
Author(s):  
Itthipol Nakamanuruck ◽  
Sompoap Talabgaew ◽  
Vichai Rungreunganun
Keyword(s):  
Preventive Maintenance ◽  
Risk Level ◽  
Reliability Engineering ◽  
Machine Maintenance ◽  
Machine Failure ◽  
Reliability Centered Maintenance ◽  
Maintenance Program ◽  
Refinery Plant ◽  
Maintenance Plan

This research aims for guidance in improving the productivity of the machines in case study of refinery plant based on the principle of preventive maintenance of machinery applications. This is to increase the availability and improve the reliability and overall equipment effectiveness (OEE) of the machines. From the data collected showed that high cost of maintenance is quite high caused by non-standard maintenance and no prioritization of machine maintenance. Therefore, the researchers proposed a maintenance program developed based on reliability engineering with failure mode and effects analysis (FMEA). FMEA was the first brought to analyze the root causes of machine failure and to evaluate the risk priority number (RPN). The data was performed preventive maintenance plan standardized the maintenance system in order to optimize maintenance task and maximize the efficiency of machinery. After applying FMEA, the result showed that the chance of failure in equipment was very low (1 time in 12 years) after the scheduled maintenance plan and opportunities to detect damage in advance was moderate to high. Therefore, the equipment with a moderate to high risk is likely more damage than the first round of maintenance (5 years). Moreover, the average of the residual risk level analysis of the machine decreased by 59.165% and overall equipment effectiveness (OEE) increased from 92.66% to 98%

scholarly journals Integration of maintenance systems

2018 ◽  
Vol 211 ◽  
pp. 03010
Author(s):  
S H Sarje
Keyword(s):  
Preventive Maintenance ◽  
Hazard Rate ◽  
Reduction Factor ◽  
Maintenance Cost ◽  
Cost Ratio ◽  
Imperfect Maintenance ◽  
Maintenance Policy ◽  
Maintenance System ◽  
Reliability Centered Maintenance ◽  
High Flexibility

Excellence in maintenance is imperative in highly competitive market because it resulted into minimum maintenance cost, high equipment effectiveness, maximum reliability of the system, high quality of the products, low delivery time, high flexibility, safety etc. Any maintenance system such as Total Productive Maintenance (TPM) or Reliability Centered Maintenance (RCM) or Condition Based Maintenance (CBM) alone cannot achieve the excellence in maintenance but its integration may do. In this paper, an integration of TPM, RCM and CBM is proposed with a maintenance policy to take advantage of their respective strengths. A continuously monitored system subject to degradation due to the imperfect maintenance, where a hybrid hazard rate based on the concept of age reduction factor and hazard rate increase factor to predict the evolution of the system reliability in different maintenance cycles has been assumed.A quantitative decision making model for an integrated maintenance system is derived in order to assess the performance of the proposed maintenance policy. Numerical examples of calculation of optimal preventive maintenance age x and preventive maintenance number N* for the given cost ratio of corrective replacement and predictive preventive maintenance are given.

scholarly journals Optimal Maintenance Thresholds to Perform Preventive Actions by Using Multi-Objective Evolutionary Algorithms

2019 ◽  
Vol 9 (15) ◽  
pp. 3068 ◽  
Author(s):  
Aitor Goti ◽  
Aitor Oyarbide-Zubillaga ◽  
Elisabete Alberdi ◽  
Ana Sanchez ◽  
Pablo Garcia-Bringas
Keyword(s):  
Preventive Maintenance ◽  
Manufacturing Industry ◽  
Industrial Revolution ◽  
Preventive Intervention ◽  
Cost Benefit ◽  
Economic Consequences ◽  
Maintenance Cost ◽  
Optimization Approach ◽  
Multi Objective ◽  
Optimal Maintenance

Maintenance has always been a key activity in the manufacturing industry because of its economic consequences. Nowadays, its importance is increasing thanks to the “Industry 4.0” or “fourth industrial revolution”. There are more and more complex systems to maintain, and maintenance management must gain efficiency and effectiveness in order to keep all these devices in proper conditions. Within maintenance, Condition-Based Maintenance (CBM) programs can provide significant advantages, even though often these programs are complex to manage and understand. For this reason, several research papers propose approaches that are as simple as possible and can be understood by users and modified by experts. In this context, this paper focuses on CBM optimization in an industrial environment, with the objective of determining the optimal values of preventive intervention limits for equipment under corrective and preventive maintenance cost criteria. In this work, a cost-benefit mathematical model is developed. It considers the evolution in quality and production speed, along with condition based, corrective and preventive maintenance. The cost-benefit optimization is performed using a Multi-Objective Evolutionary Algorithm. Both the model and the optimization approach are applied to an industrial case.

Risk analysis for lot-sizing and maintenance optimization problem under energy constraint with subcontractor solution

2020 ◽  
Vol 37 (6/7) ◽  
pp. 873-904
Author(s):  
Mohamed Ali Kammoun ◽  
Zied Hajej ◽  
Nidhal Rezg
Keyword(s):  
Risk Analysis ◽  
Preventive Maintenance ◽  
Lot Sizing ◽  
Maintenance Cost ◽  
Lot Size ◽  
Content Type ◽  
Analysis Study ◽  
Dynamic Lot Sizing ◽  
Maintenance Plan ◽  
Maintenance Problem

PurposeThe main contribution of this manuscript is to suggest new approaches in order to deal with dynamic lot-sizing and maintenance problem under aspect energetic and risk analysis. The authors introduce a new maintenance strategy based on the centroid approach to determine a common preventive maintenance plan for all machines to minimize the total maintenance cost. Thereafter, the authors suggest a risk analysis study further to unforeseen disruption of availability machines with the aim of helping the production stakeholders to achieve the obtained forecasting lot-size plan.Design/methodology/approachThe authors tackle the dynamic lot-sizing problem using an efficient hybrid approach based on random exploration and branch and bound method to generate possible solutions. Indeed, the feasible solutions of random exploration method are used as input for branch and bound to determine the near-optimal solution of lot-size plan. In addition, our contribution to the maintenance part is to determine the optimal common maintenance plan for M machines based on a new algorithm called preventive maintenance (PM) periods means.FindingsFirst, the authors have funded the optimal lot-size plan that should satisfy the random demand under service level requirement and energy constraint while minimizing the costs of production and inventory. Indeed, establishing a best lot-size plan is to determine the appropriate number of available machines and manufactured units per period. Second, for risk analysis study, the solution of subcontracting is proposed by specifying a maximum cost of subcontractor in the context of a calling of tenders.Originality/valueFor maintenance problem, the originality consists in regrouping the maintenance plans of M machines into only one plan. This approach lets us to minimize the total maintenance cost and reduces the frequent breaks of production. As a second part, this paper contributed to the development of a new risk analysis study further to unforeseen disruption of availability machines. This risk analysis developed a decision-making system, for production stakeholders, in order to achieve the forecasting lot-size plan and keeps its profitability, by specifying the unit cost threshold of subcontractor in the context of a calling of tender.

scholarly journals Optimal Preventive Maintenance of Wind Turbine Components with Imperfect Continuous Condition Monitoring

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3801 ◽  
Author(s):  
Ahmed Raza ◽  
Vladimir Ulansky
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Preventive Maintenance ◽  
False Negative ◽  
Maintenance Cost ◽  
Arbitrary Distribution ◽  
Average Lifetime ◽  
True Negative ◽  
Degradation Failure ◽  
Lifetime Maintenance

Among the different maintenance techniques applied to wind turbine (WT) components, online condition monitoring is probably the most promising technique. The maintenance models based on online condition monitoring have been examined in many studies. However, no study has considered preventive maintenance models with incorporated probabilities of correct and incorrect decisions made during continuous condition monitoring. This article presents a mathematical model of preventive maintenance, with imperfect continuous condition monitoring of the WT components. For the first time, the article introduces generalized expressions for calculating the interval probabilities of false positive, true positive, false negative, and true negative when continuously monitoring the condition of a WT component. Mathematical equations that allow for calculating the expected cost of maintenance per unit of time and the average lifetime maintenance cost are derived for an arbitrary distribution of time to degradation failure. A numerical example of WT blades maintenance illustrates that preventive maintenance with online condition monitoring reduces the average lifetime maintenance cost by 11.8 times, as compared to corrective maintenance, and by at least 4.2 and 2.6 times, compared with predetermined preventive maintenance for low and high crack initiation rates, respectively.

Analisis Pemeliharaan Mesin Conveyor Menggunakan Metode Preventive dan Breakdown Maintenance untuk Meminimumkan Biaya Pemeliharaan Mesin pada PT X

2021 ◽  
Vol 1 (1) ◽  
pp. 1-9
Author(s):  
Meli Amelia ◽  
Tasya Aspiranti
Keyword(s):  
Data Analysis ◽  
Preventive Maintenance ◽  
Research Method ◽  
Research Data ◽  
Maintenance Cost ◽  
Know How ◽  
Descriptive Research ◽  
Quantitative Descriptive ◽  
Engine Maintenance ◽  
Care Study

Abstract. This research aims to know how the implementation of maintenance conducted by PT X and how maintenance by PT X used the preventive and breakdown maintenance methods to minimize engine maintenance cost. The research method used in this study is care study whereas this type of research is quantitative descriptive research. Technique of collecting data in this research by obsererving, interviewing and collecting documents related to research. Data analysis used by using preventive and breakdown maintenance methods. The result of this research is PT X performs maintenance of the engine by using preventive maintenance such as routine maintenance, semi-overhaul forecast maintenance and annual maintenance and breakdown maintenance are usually performed when the machine is fully damaged or dead. PT X should implement preventive maintenance because it is more efficient at 13,2% than the company’s maintenance. Abstrak. Penelitian ini bertujuan untuk mengetahui bagaimana pelaksanaan pemeliharaan mesin yang dilakukan PT X dan bagaimana pemeliharaan mesin yang yang dilakukan PT X dengan menggunakan metode preventive dan breakdown maintenance untuk meminimumkan biaya pemeliharaan mesin. Metode penelitian yang dilakukan dalam penelitian ini studi kasus sedangkan jenis penelitian ini adalah penelitian deskriptif kuantitatif. Teknik pengumpulan data dalam penelitian ini dengan melakukan observasi, wawancara dan pengumpulan dokumen-dokumen yang berkaitan dengan penelitian. Analisis data yang digunakan dengan menggunakan metode preventive dan breakdown maintenance. Hasil dari penelitian ini adalah PT X hendaknya melakukan pemeliharaan mesin dengan menggunakan preventive maintenance seperti perawatan rutin, perawatan semi overhaul dan perawatan tahunan dan breakdown maintenance biasa dilakukan saat mesin mengalami kerusakan atau mati total. PT X hendaknya melaksanakan preventive maintenance karena lebih efisien sebesar 13,2% dibandingkan pemeliharaan yang dilakukan perusahaan.

Reliability Analysis Using Deep Learning

2018 ◽  
Author(s):  
Chong Chen ◽  
Ying Liu ◽  
Xianfang Sun ◽  
Shixuan Wang ◽  
Carla Di Cairano-Gilfedder ◽  
...  
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Reliability Analysis ◽  
General Principle ◽  
Preventive Maintenance ◽  
Maintenance Cost ◽  
Empirical Results ◽  
Time Between Failures ◽  
Learning Techniques ◽  
Deep Learning Model

Over the last few decades, reliability analysis has gained more and more attention as it can be beneficial in lowering the maintenance cost. Time between failures (TBF) is an essential topic in reliability analysis. If the TBF can be accurately predicted, preventive maintenance can be scheduled in advance in order to avoid critical failures. The purpose of this paper is to research the TBF using deep learning techniques. Deep learning, as a tool capable of capturing the highly complex and nonlinearly patterns, can be a useful tool for TBF prediction. The general principle of how to design deep learning model was introduced. By using a sizeable amount of automobile TBF dataset, we conduct an experiential study on TBF prediction by deep learning and several data mining approaches. The empirical results show the merits of deep learning in performance but comes with cost of high computational load.

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.

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