Multi-objective optimization for preventive maintenance of offshore safety critical equipment integrating dynamic risk and maintenance cost

2022 ◽  
Vol 245 ◽  
pp. 110557
Author(s):  
Yue Han ◽  
Xingwei Zhen ◽  
Yi Huang
Keyword(s):  
Preventive Maintenance ◽  
Maintenance Cost ◽  
Multi Objective Optimization ◽  
Safety Critical ◽  
Multi Objective ◽  
Dynamic Risk

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.

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 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.

Multi-Objective Robust Optimization of Air Engine Using IOSO Technology

2004 ◽  
Author(s):  
Egorov N. Igor ◽  
Kretinin V. Gennady ◽  
Leshchenko A. Igor ◽  
Kuptzov V. Sergey
Keyword(s):  
Fuel Consumption ◽  
Engine Performance ◽  
Optimal Solution ◽  
Performance Characteristics ◽  
Robust Design Optimization ◽  
Maintenance Cost ◽  
Pareto Optimum ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Aircraft System

This paper demonstrates the multi-objective optimization of air engine in aircraft system using either Deterministic or Robust Design Optimization statements. The goal is to obtain the Pareto-optimum frontier for the air engine and aircraft parameters. Performance characteristics of engine include the following: specific fuel consumption; thrust, with external resistance included, for any flight operating modes of aircraft; weight; the engine size parameters; engine’s life period; level of engine noise; and maintenance costs of the engine. Performance characteristics of an aircraft include passenger-per-kilometer fuel consumption, direct maintenance expenditures, maintenance cost, terrain noise level, take-off runway length, maximum flight altitude, maximum flight Mach number for different parameters of the operation process of the engine, and the various aircraft geometry parameters. While solving a problem of optimizing an engine in an aircraft system, conditions may exist where values of objective function and constraints can not be calculated. This can be caused by both the unfeasibility of a whole system for certain combinations of design variables, and the instability of numerical schemes used as mathematical models. Such conditions can even lead to a crash of the mathematical model. The existence of such areas usually substantially complicates the solution of optimization tasks and in some cases makes it impossible to find optimal solution. The paper illustrates that IOSO algorithms can deal with such cases very efficiently. This paper presents the result of the probabilistic statement of the multi-objective optimization problem, which decreases technical risks when developing modern objects and systems with the highest level of efficiency.

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