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.

Reliability/risk centered cost effective preventive maintenance planning of generating units

2018 ◽  
Vol 35 (9) ◽  
pp. 2052-2079 ◽  
Author(s):  
Umamaheswari E. ◽  
Ganesan S. ◽  
Abirami M. ◽  
Subramanian S.
Keyword(s):  
Preventive Maintenance ◽  
Optimization Technique ◽  
Solution Space ◽  
Fuzzy Decision Making ◽  
Content Type ◽  
Multi Objective ◽  
Statistical Measures ◽  
Highly Nonlinear ◽  
Optimal Maintenance ◽  
Maintenance Schedules

Purpose Finding the optimal maintenance schedules is the primitive aim of preventive maintenance scheduling (PMS) problem dealing with the objectives of reliability, risk and cost. Most of the earlier works in the literature have focused on PMS with the objectives of leveling reserves/risk/cost independently. Nevertheless, very few publications in the current literature tackle the multi-objective PMS model with simultaneous optimization of reliability, and economic perspectives. Since, the PMS problem is highly nonlinear and complex in nature, an appropriate optimization technique is necessary to solve the problem in hand. The paper aims to discuss these issues. Design/methodology/approach The complexity of the PMS problem in power systems necessitates a simple and robust optimization tool. This paper employs the modern meta-heuristic algorithm, namely, Ant Lion Optimizer (ALO) to obtain the optimal maintenance schedules for the PMS problem. In order to extract best compromise solution in the multi-objective solution space (reliability, risk and cost), a fuzzy decision-making mechanism is incorporated with ALO (FDMALO) for solving PMS. Findings As a first attempt, the best feasible maintenance schedules are obtained for PMS problem using FDMALO in the multi-objective solution space. The statistical measures are computed for the test systems which are compared with various meta-heuristic algorithms. The applicability of the algorithm for PMS problem is validated through statistical t-test. The statistical comparison and the t-test results reveal the superiority of ALO in achieving improved solution quality. The numerical and statistical results are encouraging and indicate the viability of the proposed ALO technique. Originality/value As a maiden attempt, FDMALO is used to solve the multi-objective PMS problem. This paper fills the gap in the literature by solving the PMS problem in the multi-objective framework, with the improved quality of the statistical indices.

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.

scholarly journals Multi Equipment Condition Based Maintenance Optimization Using Multi-Objective Evolutionary Algorithms

2019 ◽  
Vol 9 (22) ◽  
pp. 4849
Author(s):  
Aitor Goti ◽  
Aitor Oyarbide-Zubillaga ◽  
Ana Sanchez ◽  
Tugce Akyazi ◽  
Elisabete Alberdi
Keyword(s):  
Preventive Intervention ◽  
Discrete Event ◽  
Condition Based Maintenance ◽  
Optimization Approach ◽  
Actual System ◽  
Multi Objective ◽  
Event Simulation ◽  
Industrial Chain ◽  
Efficiency And Effectiveness ◽  
Optimal Values

Thanks to the digitalization of industry, maintenance is a trending topic. The amount of data available for analyses and optimizations in this field has increased considerably. In addition, there are more and more complex systems to maintain, and to keep all these devices in proper conditions, which requires maintenance management to gain efficiency and effectiveness. Within maintenance, Condition-Based Maintenance (CBM) programs can provide significant advantages, but often these programs are complex to manage and understand. The problem becomes more complex when equipment is analyzed in the context of a plant, where equipment can be more or less saturated, critical regarding quality, etc. Thus, this paper focuses on CBM optimization of a full industrial chain, with the objective of determining its optimal values of preventive intervention limits for equipment under economic criteria. It develops a mathematical plus discrete-event-simulation based model that takes the evolution in quality and production speed into consideration as well as condition based, corrective and preventive maintenance. The optimization process is performed using a Multi-Objective Evolutionary Algorithm. Both the model and the optimization approach are applied to an industrial case, where the data gathered by the IoT (Internet of Things) devices at edge level can detect when some premises of the CBM model are no longer valid and request a new simulation. The simulation performed in a centralized way can thus obtain new optimal values who fit better to the actual system than the existing ones. Finally, these new optimal values can be transferred to the model whenever it is necessary. The approach developed has raised the interest of a partner of the Deusto Digital Industry Chair.

scholarly journals Preventive Maintenance Strategy of Environmental Test Chamber Based on Particle Swarm Optimization Algorithm

2022 ◽  
Vol 16 ◽  
pp. 618-627
Author(s):  
Xianwen Zhou ◽  
Chaoyang Gu ◽  
Yuyu Sun ◽  
Chengjing Han ◽  
Wei Gu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Preventive Maintenance ◽  
Particle Swarm Optimization Algorithm ◽  
Test Chamber ◽  
Maintenance Cost ◽  
Maintenance Strategy ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Environmental Test

With the development of various physical industries, people pay more attention to reliability tests and test equipment. To solve the problem of making maintenance strategy of an environmental test chamber for reliability test, a periodic preventive maintenance strategy based on RCM(Reliability Centre Maintenance) is proposed. Firstly, a multi-objective optimization model of reliability and maintenance cost is established by combining reliability theory and life distribution theory, and two objectives of equipment reliability and maintenance cost are considered. Secondly, the actual environmental test chamber fault maintenance data is analyzed, and it is found the fault distribution meets the dual parameter Weibull. Finally, the particle swarm optimization algorithm is used to solve the multi-objective model optimization, and a series of Pareto optimal solutions are obtained, that is, the number of maintenance times and the corresponding time interval in the operation cycle of the environmental test chamber, and these solutions might be good references for maintenance management personnel.

scholarly journals Optimal Maintenance Decision Method for Urban Gas Pipelines Based on as Low as Reasonably Practicable Principle

2018 ◽  
Vol 11 (1) ◽  
pp. 153 ◽  
Author(s):  
Peng Zhang ◽  
Guojin Qin ◽  
Yihuan Wang
Keyword(s):  
Decision Making ◽  
Risk Reduction ◽  
Cost Benefit Analysis ◽  
Optimization Theory ◽  
Cost Benefit ◽  
Maintenance Cost ◽  
Gas Pipelines ◽  
City Gas ◽  
Optimal Maintenance ◽  
Maintenance Decision

In the transportation process of urban gas pipelines, there are various uncontrollable risks and uncertainties possibly leading to the failure of gas pipelines and thereby serious consequences, such as city gas shutdown, nearby casualties, and environmental pollution. To avoid these hazards, numerous studies have been performed in identifying and evaluating the occurrence of risks and uncertainties to pipelines. However, discussions on risk reduction and other maintenance work are scarce; therefore, a scientific method to guide decision making is non-existent, thereby resulting in excessive investment in maintenance and reduced maintenance cost of other infrastructures. Therefore, the as low as reasonably practicable (ALARP) principle combined with optimization theory is used to discuss pipeline maintenance decision-making methods in unacceptable regions and ALARP regions. This paper focuses on the analysis of pipeline risk reduction in the ALARP region and proposes three optimization decision models. The case study shows that maintenance decision making should consider the comprehensive impact of maintenance cost to reduce risk and loss cost caused by pipeline failure, and that the further cost–benefit analysis of measures should be performed. The proposed pipeline maintenance decision-making method is an economical method for pipeline operators to make risk decisions under the premise of pipeline safety, which can improve the effectiveness of the use of maintenance resources.

Multi-Objective Optimization of Inertia Equilibrium in Ultra High Speed Stamping Machine

2011 ◽  
Vol 101-102 ◽  
pp. 306-310
Author(s):  
Jian Yu Bai ◽  
Sen Lin Tong ◽  
Di Zheng
Keyword(s):  
High Speed ◽  
Manufacturing Industry ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Ultra High Speed ◽  
Machine Simulation ◽  
Simulation Results

With the development of manufacturing industry, the stroke speed of stamping machine is higher and higher. In accordance with the development, researchers and engineers have paid more and more attention to developing various types of ultra high speed stamping machines. The key to develop ultra high speed stamping machine is to solve the problem of inertia equilibrium. This paper presents an optimization approach for multi-objective inertia equilibrium design for ultra high speed stamping machine. Simulation results demonstrate the effectiveness of the proposed approach.

scholarly journals Optimal Operation of Multiple Energy System Based on Multi-Objective Theory and Grey Theory

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 68
Author(s):  
Bo Hu ◽  
Nan Wang ◽  
Zaiming Yu ◽  
Yunqing Cao ◽  
Dongsheng Yang ◽  
...  
Keyword(s):  
Environmental Pollution ◽  
Energy Management ◽  
Manufacturing Industry ◽  
Energy System ◽  
Optimal Operation ◽  
Operating Conditions ◽  
Maintenance Cost ◽  
Grey Theory ◽  
Multi Objective ◽  
Electricity Cost

The manufacturing industry consumes electricity and natural gas to provide the power and heat required for manufacturing. Additionally, large amounts of electric energy and heat energy are used, and the electricity cost, amount of environmental pollution, and equipment maintenance cost are high. Thus, optimizing the management of equipment with new energy is important to satisfy the load demand from the system. This paper formulates the scheduling problem of these multiple energy systems as a multi-objective linear regression model (MLRM), and an energy management system is designed focusing on the economy and on greenhouse gas emissions. Furthermore, a variety of optimization objectives and constraints are proposed to make the energy management scheme more practical. Then, grey theory is combined with the common MLRM to accurately represent the uncertainty in the system and to make the model better reflect the actual situation. This paper takes load fluctuation, total grid operation cost, and environmental pollution value as reference standards to measure the effect of the gray optimization algorithm. Lastly, the model is applied to optimize the energy supply plan and its performance is demonstrated using numerical examples. The verification results meet the optimized operating conditions of the multi-energy microgrid system.

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.

Comparative Analysis of Existing and Optimal Maintenance Policy of Water Borehole Schemes in South Eastern States of Nigeria

2021 ◽  
Vol 18 (1) ◽  
pp. 43-50
Author(s):  
M.C. Nwachukwu ◽  
J.C. Agunwamba ◽  
B.C. Okoro ◽  
C.N. Mama
Keyword(s):  
Comparative Analysis ◽  
Preventive Maintenance ◽  
Current Practice ◽  
Maintenance Cost ◽  
Operational Cost ◽  
Submersible Pump ◽  
Maintenance Policy ◽  
South Eastern ◽  
Optimal Maintenance ◽  
Optimal Maintenance Policy

A study optimising maintenance cost of water borehole schemes in South Eastern states of Nigeria (Abia, Anambra, Ebonyi, Enugu and Imo States) was carried out. Data was collected from 260 boreholes spread across all local government areas in the states. Optimisation results showed that for boreholes (submersible pumps) pumping once per day, the optimal preventive maintenance frequency and resulting savings in cost are 2 and ₦521,076 for Abia; 2 and ₦783,963 for Anambra; 2 and ₦458,242 for Ebonyi; 2 and ₦740,964 for Enugu; 2 and ₦605,187 Imo. For boreholes pumping twice per day, the optimal preventive maintenance frequency and resulting savings in cost are 5 and ₦1,896,301 for Abia; 4 and ₦3,692,655 for Anambra; 5 and ₦786,913 for Ebonyi; 4 and ₦4,187,161 for Enugu; 4 and ₦2,477,609 for Imo; and for boreholes pumping thrice per day; 8 and ₦2,798,330 for Abia; 7 and ₦8,372,862 for Anambra; 7 and ₦6,485,293 for Ebonyi; 10 and ₦4,014,240 for Enugu; 10 and ₦6,021,503 for Imo; with no downtime as opposed to the wasteful current practice of no preventive maintenance with downtime of up to 12 months or more. As a recommendation for a borehole scheme, there should be a check on the type of submersible pump and generator capacity as the choice made directly affects the total operational cost.

Sign in / Sign up

Export Citation Format

Share Document