Identifying New Technique for Optimizing the Maintenance Strategies of Electrical Mobile Working Machines
Appoh, Yao Kossonou (2023)
2023
Master's Programme in Security and Safety Management
Johtamisen ja talouden tiedekunta - Faculty of Management and Business
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Hyväksymispäivämäärä
2023-11-21
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202310128802
https://urn.fi/URN:NBN:fi:tuni-202310128802
Tiivistelmä
As a way to contribute to resolving the current climate issues, automobile industries
invest heavily in the development of electrical mobile working machines (loaders, drilling and bolting rigs and haul trucks). A key aspect of these new technologies that should be addressed is their maintenance management, as the existing maintenance models are mainly focused on fuel engines. As a result, it is necessary to develop a maintenance strategy to optimize availability of heavy electrical working machines and reduce operating and maintenance costs. After examining the existing maintenance strategies and maintenance planning for fuel engines, this paper presents a maintenance model, that optimizes the maintenance strategies of electrical mobile working machines using Markov decision processes. Based on the machine's condition and the maintenance costs, an optimal cost-effective maintenance policy is determined, which includes the appropriate maintenance action to be taken. The differential equations and policy iteration were solved using MATLAB and Maple V to test the model's effectiveness and implementation.
The study proposes a new method for determining transition probabilities and the optimal policy that gives the effective maintenance action to consider. Using the policy iteration algorithm the machine's optimal maintenance action and associated costs are determined when it is in the specific state, supporting the maintenance expert. In addition, the model developed in this study can be beneficial to industries managing maintenance for heavy electrical mobile working machines. Therefore, maintenance experts are able to save time and money by knowing when to perform a maintenance action and what action is appropriate.
As a result of the lack of data from the industries and no physical interaction with heavy electrical mobile working machines experts, evaluation of the findings was challenging. As a result, there is a need for future studies to improve the model and the resolution method by evaluating its validity and reliability using original data and interviews with experts from industries in heavy electrical mobile working machines manufacturing.
invest heavily in the development of electrical mobile working machines (loaders, drilling and bolting rigs and haul trucks). A key aspect of these new technologies that should be addressed is their maintenance management, as the existing maintenance models are mainly focused on fuel engines. As a result, it is necessary to develop a maintenance strategy to optimize availability of heavy electrical working machines and reduce operating and maintenance costs. After examining the existing maintenance strategies and maintenance planning for fuel engines, this paper presents a maintenance model, that optimizes the maintenance strategies of electrical mobile working machines using Markov decision processes. Based on the machine's condition and the maintenance costs, an optimal cost-effective maintenance policy is determined, which includes the appropriate maintenance action to be taken. The differential equations and policy iteration were solved using MATLAB and Maple V to test the model's effectiveness and implementation.
The study proposes a new method for determining transition probabilities and the optimal policy that gives the effective maintenance action to consider. Using the policy iteration algorithm the machine's optimal maintenance action and associated costs are determined when it is in the specific state, supporting the maintenance expert. In addition, the model developed in this study can be beneficial to industries managing maintenance for heavy electrical mobile working machines. Therefore, maintenance experts are able to save time and money by knowing when to perform a maintenance action and what action is appropriate.
As a result of the lack of data from the industries and no physical interaction with heavy electrical mobile working machines experts, evaluation of the findings was challenging. As a result, there is a need for future studies to improve the model and the resolution method by evaluating its validity and reliability using original data and interviews with experts from industries in heavy electrical mobile working machines manufacturing.