Multi-component maintenance grouping optimization based on stochastic dependency

2020 ◽  
pp. 1748006X2094751
Author(s):  
Vimal Vijayan ◽  
Sanjay K Chaturvedi
Keyword(s):  
Diesel Engine ◽  
Cost Optimization ◽  
Maintenance Cost ◽  
Substantial Cost ◽  
Maintenance Time ◽  
Optimal Maintenance ◽  
Air Intake ◽  
Maintenance Activities ◽  
Preparatory Work

Maintenance activities often require an identical preparatory work. Therefore, a joint execution of such maintenance activities may save a substantial cost. In this work, we consider the problem of optimizing the total maintenance cost of a multi-component repairable system by grouping of components and carrying out maintenance activities on group(s) of components of a complex system. More specifically, we propose a maintenance grouping cost optimization model based on the stochastic dependency as well as economic dependency among components in a system. The stochastic dependency modeling is done using Bayesian network by considering the failure probability of components as a measure of failure interactions among components. Penalty functions are formulated due to the shift of individual optimal maintenance time of components to find the optimum joint maintenance interval and associated cost benefits. Finally, a case study on a diesel engine of a diesel power plant involving 10 components (components of diesel engine, air intake system, and turbocharger) is presented to illustrate the proposed approach.

scholarly journals AN INTEGRATED MULTI-OBJECTIVES OPTIMIZATION APPROACH ON MODELLING PAVEMENT MAINTENANCE STRATEGIES FOR PAVEMENT SUSTAINABILITY

2020 ◽  
Vol 26 (8) ◽  
pp. 717-732
Author(s):  
Ankang Ji ◽  
Xiaolong Xue ◽  
Yuna Wang ◽  
Xiaowei Luo ◽  
Minggong Zhang
Keyword(s):  
Integrated Approach ◽  
Maintenance Cost ◽  
First Year ◽  
Optimization Approach ◽  
Maintenance Strategies ◽  
Pavement Condition ◽  
Pavement Maintenance ◽  
Road Pavement ◽  
Maintenance Activities

Addressing the multi-dimensional challenges to promote pavement sustainability requires the development of an optimization approach by simultaneously taking into account future pavement conditions for pavement maintenance with the capability to search and determine optimal pavement maintenance strategies. Thus, this research presents an integrated approach based on the Markov chain and Particle swarm optimization algorithm which aims to consider the predicted pavement condition and optimize the pavement maintenance strategies during operation when applied in the maintenance management of a road pavement section. A case study is conducted for testing the capability of the proposed integrated approach based on two maintenance perspectives. For case 1, maintenance activities mainly occur in TM20, TM31, and TM41, with the maximum maintenance mileage reaching 88.49 miles, 50.89 miles, and 20.91 miles, respectively. For case 2, the largest annual maintenance cost in the first year is $15.16 million with four types of maintenance activities. Thereafter, the maintenance activities are performed at TM10, TM31, and TM41, respectively. The results obtained, compared with the linear program, show the integrated approach is effective and reliable for determining the maintenance strategy that can be employed to promote pavement sustainability.

An integrated space-based building maintenance management model using multi-objective optimization

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Huthaifa AL-Smadi ◽  
Abobakr Al-Sakkaf ◽  
Tarek Zayed ◽  
Fuzhan Nasiri
Keyword(s):  
Residential Buildings ◽  
Maintenance Cost ◽  
Content Type ◽  
Fund Allocation ◽  
Building Maintenance ◽  
Window System ◽  
Optimal Maintenance ◽  
Building Condition ◽  
Maintenance Model

PurposeThe purpose of this study is to minimize cost and minimize building condition. Weibull distribution approach was employed to generate deterioration curves over time. The third floor of Concordia University’s Engineering And Visual Arts (EV) Complex in Montreal, Canada, served as a case study to test the maintenance model and determine the optimal maintenance activities to be performed.Design/methodology/approachThis research has demonstrated that there is insufficient fund allocation for the maintenance of non-residential buildings. Therefore, this research focused on designing and developing a maintenance optimization model that provides the type of spaces (architectural system) in a building. Sensitivity analysis was used to calculate weights to validate the model. Particle swarm optimization, based explicitly on multiple objectives, was applied for the optimization problem using MATLAB.FindingsFollowing 100 iterations, 13 non-dominant solutions were generated. Not only was the overall maintenance cost minimized, but the condition of the building was also maximized. Moreover, the condition prediction model demonstrated that the window system type has the most rapid deterioration in educational buildings.Originality/valueThe model is flexible and can be modified by facility managers to align with the required codes or standards.

scholarly journals Optimal Maintenance Probabilities and Preventive Replacement Maintenance Policy for Photocopy Machines

2021 ◽  
Author(s):  
Nse Udoh ◽  
Akaninyene Udom ◽  
Fredrick Ohaegbunem
Keyword(s):  
Minimum Cost ◽  
Maintenance Cost ◽  
Logistic Distribution ◽  
Maintenance Policy ◽  
Preventive Replacement ◽  
Maintenance Time ◽  
Replacement Model ◽  
Cumulative Function ◽  
Optimal Maintenance ◽  
Preventive Maintenance Policy

The need for suitable replacement policies are essential to minimize down time, maintenance cost and maximize the availability and reliability of equipment. On this premise, this work models the failure rate of Photocopy machines and obtain its optimal preventive maintenance policy that would prevent damage and its attendant losses to both users and end-product consumers. The failure distribution of the machine was shown to follow the Log-Logistic distribution with shape parameter, αˆ=1.723339368 and scale parameter, βˆ=763.9219635. Optimal probabilities of the distribution were obtained and utilized in both the cumulative failure function and cumulative hazard function-based replacement models to formulate a replacement maintenance policy for the machine. The failure cumulative function-based replacement model was found to be a better model which yields optimal replacement maintenance time of 166 hours at a minimum cost of 113 Naira for maintaining the machine per cycle time with 96% availability, 94% reliability and 0.07% chance of failure occurrence in the machine.

Optimal track geometry maintenance limits using machine learning: A case study

2020 ◽  
pp. 095440972097009
Author(s):  
Ahmad Kasraei ◽  
Jabbar Ali Zakeri ◽  
Arash Bakhtiary
Keyword(s):  
Clustering Algorithm ◽  
Cost Model ◽  
Maintenance Cost ◽  
Maintenance Costs ◽  
Corrective Maintenance ◽  
Track Geometry ◽  
Penalty Costs ◽  
Optimal Maintenance ◽  
Maintenance Activities ◽  
Maintenance Model

The aim of this study has been to determine the optimal maintenance limits for one of the main railway lines in Iran in such a way that the total maintenance costs are minimized. For this purpose, a cost model has been developed by considering costs related to preventive maintenance activities, corrective maintenance activities, inspection, and a penalty costs associated with exceeding corrective maintenance limit. Standard deviation of longitudinal level was used to measure the quality of track geometry. In order to reduce the level of uncertainty in the maintenance model, K-means clustering algorithm was used to classify track sections with most similarity. Then, a linear function was used for each cluster to model the degradation of track sections. Monte Carlo technique was used to simulate track geometry behavior and determine the optimal maintenance limit which minimizes the total maintenance costs. The results of this paper show that setting an optimal limit can affect total annual maintenance cost about 27 to 57 percent.

scholarly journals A Study on the Predictive Maintenance Method of Fully Mechanized Mining Equipment based on Cost and Time Factors

2021 ◽  
Author(s):  
Xiangang Cao ◽  
Tianbo Xu ◽  
Youjun Zhao ◽  
Jiangbin Zhao ◽  
Yan Wang
Keyword(s):  
Time Factors ◽  
Predictive Maintenance ◽  
Maintenance Cost ◽  
Maintenance Planning ◽  
Mining Equipment ◽  
Cost Rate ◽  
Maintenance Time ◽  
Maintenance Cycle ◽  
Optimal Maintenance ◽  
Single Objective

Abstract In view of the problems of excessive maintenance and insufficient utilization of equipment service life caused by preventive maintenance of fully mechanized mining equipment with fixed cycle, a predictive maintenance method is proposed. Firstly, based on Weibull distribution function and evolution rules of equipment decay, the evolution model of equipment failure rate is established; Then, the single-objective decision-making models of equipment maintenance cost rate and maintenance downtime rate are established respectively. On this basis, the multi-objective predictive maintenance planning model of fully mechanized mining equipment with comprehensive cost and time factors is established, and the optimal predictive maintenance cycle planning sequence is obtained. Combined with the coal production continuation plan, this paper puts forward a method to determine the optimal maintenance time by making suitable choices between advance maintenance and delay maintenance. The result confirms the effectiveness and superiority of the proposed method.

scholarly journals Reliability-Based Opportunistic Maintenance Modeling for Multi-component Systems with Economic Dependence under Base Warranty

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Rongcai Wang ◽  
Zhonghua Cheng ◽  
Enzhi Dong ◽  
Chiming Guo ◽  
Liqing Rong
Keyword(s):  
Production System ◽  
Cost Optimization ◽  
Economic Losses ◽  
Maintenance Cost ◽  
Economic Dependence ◽  
Opportunistic Maintenance ◽  
Component Systems ◽  
Important Solution ◽  
Optimal Maintenance ◽  
Warranty Cost

Maintenance usually plays a key role in controlling a multi-component production system within normal operations. Furthermore, the failure of components in the production system will also cause large economic losses for users due to the shutdown. Meanwhile, manufacturers of the production system will be confronted with the challenges of the warranty cost. Therefore, it is of great significance to optimize the maintenance strategy to reduce the downtime and warranty cost of the system. Opportunistic maintenance (OM) is a quite important solution to reduce the maintenance cost and improve the system performance. This paper studies the OM problem for multi-component systems with economic dependence under base warranty (BW). The irregular imperfect preventive maintenance (PM) is performed to reduce the failure rate of components at a certain PM reliability threshold. Moreover, the OM optimization model is developed to minimize the maintenance cost under the optimal OM reliability threshold of each component. A simulated annealing (SA) algorithm is proposed to determine the optimal maintenance cost of the system and the optimal OM threshold under BW. Finally, a numerical example of a belt conveyor drive device in a port is introduced to demonstrate the feasibility and advantages of the proposed model in maintenance cost optimization.

scholarly journals Optimization method of fatigue maintenance for cable-beam anchorage zone of suspension bridge

2020 ◽  
Vol 103 (3) ◽  
pp. 003685042095013
Author(s):  
Hongmei Tan ◽  
Yong Zeng ◽  
Qianping Zhang
Keyword(s):  
Failure Probability ◽  
Life Cycle Cost ◽  
Structural Reliability ◽  
Relative Proportion ◽  
Suspension Bridge ◽  
Optimization Method ◽  
Maintenance Cost ◽  
Time Interval ◽  
Maintenance Time ◽  
Optimal Maintenance

The cable-beam anchorage zone is the vital load-bearing component in suspension bridge. For maintenance of such structure, a method of probabilistic optimization was proposed by combining linear elastic fracture mechanics, structural reliability and life cycle cost analysis. In this method, the optimal maintenance time is obtained by determining the relative proportion between the costs under the condition that the structural reliability is higher than the minimum allowable reliability. While, the minimum total maintenance cost is obtained by determining the maintenance interval. Then, an example is presented to verify this method, with the following conclusions: the reliability index is inversely proportional to the failure probability, the change of maintenance cost and failure cost affects the optimal maintenance time, the optimal maintenance time will be ahead of time when consider the risk cost. And finally, when the maintenance time interval is determined, the optimal maintenance cost is affected by the maintenance probability and the failure probability.

scholarly journals The Proposed Maintenance Task for Plastic Injection Machine Using Reliability and Risk Centered Maintenance (RRCM) method in Manufacturing Industry

2019 ◽  
Vol 10 (2) ◽  
Author(s):  
Liza Nafiah Maulidina ◽  
Fransiskus Tatas Dwi Atmaji ◽  
Judi Alhilman
Keyword(s):  
Manufacturing Industry ◽  
Maintenance Cost ◽  
Time Interval ◽  
Maintenance Policy ◽  
Risk Matrix ◽  
Maintenance Time ◽  
Optimal Maintenance ◽  
Critical Components ◽  
The Cost ◽  
Plastic Injection

The objective of this research was to determine the optimal maintenance time interval for the selected critical components and the total cost of maintenance of a plastic injection machine. In determining the critical components, a risk matrix was used, and three components were selected, namely, hydraulic hose, barrel, and motor. Using the Reliability and Risk Centered Maintenance (RRCM) method, the researchers got a proposed maintenance policy and the total maintenance cost. Based on the result, it shows that there are seven proposed maintenance tasks with three scheduled oncondition tasks and four scheduled restoration tasks with an average maintenance interval of two months. The total maintenance cost proposed is IDR91.595.318. The cost is smaller compared to the actual maintenance costs of the company.

scholarly journals Risk-Based Predictive Maintenance for Safety-Critical Systems by Using Probabilistic Inference

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Tianhua Xu ◽  
Tao Tang ◽  
Haifeng Wang ◽  
Tangming Yuan
Keyword(s):  
High Speed ◽  
Probabilistic Inference ◽  
Evolution Model ◽  
Predictive Maintenance ◽  
Maintenance Cost ◽  
Dynamic Evolution ◽  
Critical Systems ◽  
Maintenance Time ◽  
Safety Constraints ◽  
Optimal Maintenance

Risk-based maintenance (RBM) aims to improve maintenance planning and decision making by reducing the probability and consequences of failure of equipment. A new predictive maintenance strategy that integrates dynamic evolution model and risk assessment is proposed which can be used to calculate the optimal maintenance time with minimal cost and safety constraints. The dynamic evolution model provides qualified risks by using probabilistic inference with bucket elimination and gives the prospective degradation trend of a complex system. Based on the degradation trend, an optimal maintenance time can be determined by minimizing the expected maintenance cost per time unit. The effectiveness of the proposed method is validated and demonstrated by a collision accident of high-speed trains with obstacles in the presence of safety and cost constrains.

Assessing Pruning Knowledge towards Effective Tree Maintenance: A case study of four Local Authorities in Malaysia

2020 ◽  
Vol 5 (13) ◽  
pp. 223
Author(s):  
Norainiratna Badrulhisham ◽  
Noriah Othman
Keyword(s):  
Publishing House ◽  
Knowledge Level ◽  
Local Authorities ◽  
The Public ◽  
Risk Of Injury ◽  
International Publishing ◽  
Maintenance Activities ◽  
Tree Pruning ◽  
Open Access Article

Pruning is one of the most crucial tree maintenance activities which give an impact on the tree's health and structure. Besides, improper pruning will contribute to the risk of injury to property and the public. This study aims to assess pruning knowledge among four Local authorities in Malaysia. Results found that 69.3 percent of tree pruning workers have a Good pruning knowledge level. However, Topping, pruning types and pruning cut dimension shows the lowest mean percentage of the correct answer. The findings also show that there is a significant positive relationship between pruning knowledge and education level and frequency attending pruning courses.Keywords: Tree pruning; knowledge; sustainable practices; urban treeseISSN: 2398-4287 © 2020. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.DOI: https://doi.org/10.21834/e-bpj.v5i13.2054 

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