**6. Conclusions**

A methodological framework and a new preventive maintenance model were proposed that make it possible to optimize maintenance strategies in manufacturing production equipment. More generally, in the context of reducing carbon emissions and mitigating global warming, this paper focuses on solving the problem of equipment maintenance and energy efficiency in production systems by modeling and calculating the costs and various energy consumptions in the process of equipment maintenance to achieve the goal of

optimizing maintenance strategies. In addition, the difference between considering energy efficiency and not is shown in this paper. The main findings of the article are the following: (1) compared with a maintenance strategy that only considers maintenance costs, the integrated consideration of maintenance costs, energy efficiency, and product quality is more suitable for manufacturing systems; (2) the modeling of dynamic preventive maintenance costs as well as dynamic operational energy consumption makes the calculation of costs and energy consumption more accurate; (3) the recycling of defective products is consistent with the goal of energy saving and emission reduction, and the amount of recycling is closely related to the state of the equipment. The framework and methods presented in this paper can be applied to production, maintenance, quality, and architecture maintenance optimization in manufacturing, which makes it possible to support managemen<sup>t</sup> decisions. The decision process regarding production, quality control, and maintenance will be influenced by the results of the contribution. For example, the energy efficiency in maintenance will influence the maintenance policy, and the manufacturing system will specify new solutions for recycling defective products.

However, there are also limitations of the study. In many cases, manufacturing systems often include much equipment, which may be connected in series, parallel, or groups. The limitations of this paper, which considers only single-device preventive maintenance, also indicate potential directions for further research. In further research, the model can be extended to more complex equipment models and the use of opportunistic maintenance.

**Author Contributions:** Formal analysis, J.L.; Funding acquisition, T.X. and C.Y.; Investigation, Q.L.; Methodology, L.Y. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by grants from the National Key R&D Program of China (No. 2021YFF0900400), National Natural Science Foundation of China (Nos. 71840003), Natural Science Foundation of Shanghai (No. 19ZR1435600), Humanity and Social Science Planning Foundation of the Ministry of Education of China (No. 20YJAZH068), Action Plan for Scientific and Technological Innovation of Shanghai Science and Technology Commission (No. 21SQBS01404), and the Science and Technology Development Project of University of Shanghai for Technology and Science (No. 2020KJFZ038).

**Data Availability Statement:** Not applicable.

**Acknowledgments:** The authors are indebted to the reviewers and the editors for their constructive comments, which greatly improved the contents and exposition of this paper.

**Conflicts of Interest:** The authors declare no conflict of interest.
