An optimal preventive maintenance strategy for the hydraulic system of platform firefighting vehicle based on the improved NSGA-II algorithm

2019 ◽  
Vol 233 (6) ◽  
pp. 978-989 ◽  
Author(s):  
Rumin Teng ◽  
Jifei Liang ◽  
Guanglei Wu ◽  
Dianlong Wang ◽  
Xin Wang
Keyword(s):  
Preventive Maintenance ◽  
Hydraulic System ◽  
Maintenance Cost ◽  
Nsga Ii ◽  
Equipment Reliability ◽  
Equipment Availability ◽  
Age Regression ◽  
Working Age ◽  
The Difference ◽  
Crossover And Mutation

Based on the reliability analysis, this work presents a model of the preventive maintenance for the hydraulic system of platform firefighting vehicle, considering the working age regression factor and the equivalent service life. In this model, both the equipment availability and average maintenance cost are treated as the objective functions and constrained by the equipment reliability as well as the total working time. The non-dominated sorting genetic algorithm II (NSGA-II) algorithm is adopted to solve the previous optimization problem, wherein the constrained inequality functions in the stages of initialization, mutation, and crossover are introduced to assess the solution to meet the constraints or not, through the improvements in three aspects, namely, the improvement of the algorithm’s initialization to ensure the initial variables meet the equality constraint, the utilization of the mean division of the difference values to adjust the variables after the selection, and crossover and mutation of the gene. Numerical analysis is carried out to verify the feasibility of the improved algorithm, wherefrom the Pareto-optimal solutions for the preventive maintenance of the platform firefighting vehicle are obtained to provide the optimum strategy for the service engineers with different weighted factors.

Optimization of equal-cycle maintenance strategy considering imperfect preventive maintenance

2021 ◽  
pp. 095440892110638
Author(s):  
Xinlong Li ◽  
Yan Ran ◽  
Genbao Zhang
Keyword(s):  
Decision Making ◽  
Preventive Maintenance ◽  
Maintenance Cost ◽  
Maintenance Strategy ◽  
Action Unit ◽  
Cost Rate ◽  
Equipment Reliability ◽  
Optimal Maintenance ◽  
Important Means ◽  
Maintenance Decision

Preventive maintenance is an important means to extend equipment life and improve equipment reliability. Traditional preventive maintenance decision-making is often based on components or the entire system, the granularity is too large and the decision-making is not accurate enough. The meta-action unit is more refined than the component or system, so the maintenance decision-making based on the meta-action unit is more accurate. Therefore, this paper takes the meta-action unit as the research carrier, considers the imperfect preventive maintenance, based on the hybrid hazard rate model, established the imperfect preventive maintenance optimization model of the meta-action unit, and the optimization solution algorithm was given for the maintenance strategy. Finally, through numerical analysis, the validity of the model is verified, and the influence of different maintenance costs on the optimal maintenance strategy and optimal maintenance cost rate is analyzed.

Transition From Time–Based Preventive Maintenance to Condition–Based Maintenance

2017 ◽  
Author(s):  
Liu Xiaonian ◽  
Wang Liangsheng
Keyword(s):  
Preventive Maintenance ◽  
Nuclear Power ◽  
Degradation Mechanism ◽  
Pilot Project ◽  
Condition Based Maintenance ◽  
Maintenance Cost ◽  
Mechanism Analysis ◽  
Equipment Reliability ◽  
Production Scheme ◽  
A Site

An optimized maintenance strategy is being pursued in the nuclear power industry, so as to reduce the maintenance cost and improve equipment reliability. Transition from time-based preventive maintenance (TBM) to Condition-based Maintenance (CBM) or Predictive Maintenance (PdM) is being set as a site initiative by different utilities. This paper analyzes the key elements a plant should focus on to achieve a successful CBM transition; discusses the implementation steps and matters needing attention for the pilot project of CBM transition; and depicts precursors for CBM such as CBM equipment scope screening, equipment failure history collection, failure mode and degradation mechanism analysis, monitoring parameters and frequency setting, CBM result evaluation, and CBM planning. The paper also discusses the impacts and challenges of CBM transition campaign to the existing production scheme.

scholarly journals Improving Equipment Effectiveness by Designing Optimal Production Process

2011 ◽  
Vol 383-390 ◽  
pp. 6363-6369
Author(s):  
Mohammad Amin Okhovat ◽  
Taravatsadat Nehzati ◽  
Amin Pouriran ◽  
Shahriar Fakhar ◽  
Mohd Khairol Anuar Mohd Ariffin
Keyword(s):  
Manufacturing Systems ◽  
Programming Model ◽  
Mixed Integer ◽  
Maintenance Cost ◽  
Manufacturing Strategy ◽  
Total Productive Maintenance ◽  
Total Cost ◽  
Equipment Reliability ◽  
Equipment Availability ◽  
Optimal Material

Reliable manufacturing equipment is an indispensable factor to the performance and profitability of manufacturing systems. Total productive maintenance (TPM) has been recognized as a comprehensive manufacturing strategy to maximize equipment reliability and effectiveness through rooting out all manufacturing losses. Availability of equipment is a focus area in TPM to improve effectiveness throughout the lifetime of the equipment. This study develops a mixed integer linear programming model to increase equipment availability considering maintenance cost of each machine in the system. The main objective is minimizing total cost while designing optimal material flows between different operational levels of manufacturing process. A hypothetical problem is presented and solved by the developed model.

scholarly journals Importance measure-based maintenance strategy considering maintenance costs

2021 ◽  
Vol 24 (1) ◽  
pp. 15-24
Author(s):  
Chao Zhang ◽  
Yadong Zhang ◽  
Hongyan Dui ◽  
Shaoping Wang ◽  
Mileta M. Tomovic
Keyword(s):  
Preventive Maintenance ◽  
System Performance ◽  
Maintenance Cost ◽  
Importance Measure ◽  
Repairable Systems ◽  
Consistent System ◽  
System Components ◽  
System Maintenance ◽  
The Difference ◽  
Selection Of

Maintenance is an important way to ensure the best performance of repairable systems. This paper considers how to reduce system maintenance cost while ensuring consistent system performance. Due to budget constraints, preventive maintenance (PM) can be done on only some of the system components. Also, different selections of components to be maintained can have markedly different effects on system performance. On the basis of the above issues, this paper proposes an importance-based maintenance priority (IBMP) model to guide the selection of PM components. Then the model is extended to find the degree of correlation between two components to be maintained and a joint importance-based maintenance priority (JIBMP) model to guide the selection of opportunistic maintenance (OM) components is proposed. Also, optimization strategies under various conditions are proposed. Finally, a case of 2H2E architecture is used to demonstrate the proposed method. The results show that generators in the 2E layout have the highest maintenance priority, which further explains the difference in the importance of each component in PM.

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.

scholarly journals A Green Demand-Responsive Airport Shuttle Service Problem with Time-Varying Speeds

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Ming Wei ◽  
Binbin Jing ◽  
Jian Yin ◽  
Yang Zang
Keyword(s):  
Time Windows ◽  
Operating Cost ◽  
Model Performance ◽  
Path Selection ◽  
Mixed Integer ◽  
Time Varying ◽  
Alternative Fuel Vehicles ◽  
Nsga Ii ◽  
The Difference ◽  
On The Road

This study proposes a multiobjective mixed integer linear programming (MOMILP) model for a demand-responsive airport shuttle service. The approach aims to assign a set of alternative fuel vehicles (AFVs) located at different depots to visit each demand point within the specified time and transport all of them to the airport. The proposed model effectively captures the interactions between path selection and environmental protection. Moreover, users with flexible pick-up time windows, the time-varying speed of vehicles on the road network, and the limited fuel for the route duration are also fully considered in this model. The work aims at simultaneously minimizing the operating cost, vehicle fuel consumption, and CO2 emissions. Since this task is an NP-hard problem, a heuristic-based nondominated sorting genetic algorithm (NSGA-II) is also presented to find Pareto optimal solutions in a reasonable amount of time. Finally, a real-world example is provided to illustrate the proposed methodology. The results demonstrate that the model not only selects an optimal depot for each AFV but also determines its route and timetable plan. A sensitivity analysis is also given to assess the effect of early/late arrival penalty weights and the number of AFVs on the model performance, and the difference in quality between the proposed and traditional models is compared.

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