4.7 Article

A method for economic evaluation of predictive maintenance technologies by integrating system dynamics and evolutionary game modelling

期刊

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.ress.2022.108424

关键词

Predictive maintenance technology; Economic evaluation; Strategy optimisation; System dynamics; Evolutionary game; Lithium-ion battery

资金

  1. National Natural Science Foundation of China [52004030]
  2. Beijing Institute of Technology Research Fund Program for Young Scholars [XSQD202002007]
  3. Fundamental Research Funds for Beijing University of Civil Engineering and Architecture [X21056]
  4. Open Research Fund Program of Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles [PGU2020K007]

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Predictive maintenance technologies can aid in failure prediction and system management, but the additional cost of establishing the system can hinder widespread application. The decision to adopt this technology can be based on the computation of return on investment.
Predictive maintenance technologies can be employed for failure prediction and system health management. Nevertheless, the additional cost involved in establishing the predictive maintenance system can be an obstacle to its widespread application. The decision on the predictive maintenance technology adoption can be made through the computation of the return on investment. To investigate the mechanisms of dynamic game between stakeholders involved in predictive maintenance, we establish the SD-EGT model from the perspective of systems engineering. This paper aims to propose an integrated method for the economic evaluation of predictive maintenance technologies by considering the incremental costs and benefits associated with its deployment. As an exemplary case, we take the Lithium-ion batteries whose failures have led to unexpected safety accidents. Firstly, we construct a quantitative relationship model between the failure modes and the predictive benefits of Lithium-ion battery systems to quantify the incremental benefits. Then, we establish a cost-benefit analysis (CBA) model by using system dynamics (SD) to make decisions about cost-effectiveness. Secondly, to optimize the cost investment strategy for the predictive maintenance technology, we develop an enterprise-government evolutionary game model, considering the information asymmetry between players. Eventually, we conduct a sensitivity analysis of the static subsidy strategy. The proposed methodology is serviceable to optimize the decision-making of predictive maintenance technology investment, which is a difficult yet very important task in industrial practice.

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