## 6 Discussion and Conclusions  
This paper aimed to propose a fault classification method and fault identification model for CPSs in dam engineering, which is of great significance for mastering the influence degree of CPS faults and creating a maintenance plan for different parts. An on-site investigation of production system faults in a concrete dam project under construction was conducted. Combining production system fault data and dam-pouring information, the influence law of production system faults on the dam-pouring construction progress, quality, and efficiency was discussed. Based on the concrete production logs of multiple concrete dams and a literature review, a fault classification system for a concrete dam production system was proposed by comprehensively considering the mechanical structure characteristics and operating characteristics. The problem of the classification of faults not being clear and restricting the further study of the impact law of faults is solved. Based on the GCA method, a fault identification model of the CPS was established. The correlation of  
Figure 8: Comparison of the grey correlation degree of the influencing factors of the CPS fault. the fault statistical sample data was analyzed. The identification of key influencing factors of various faults was realized. (1) Based on the literature data and actual engineering CPS fault statistics, according to the location of a fault, the faults of CPSs were divided into four categories: batch metering system faults, material transport system faults, pneumatic system faults, and mixer faults. According to the specific manifestation of faults, comprehensively considering the mechanical structure characteristics and operational characteristics, the faults were subdivided into 22 sub-categories. (2) Through a literature review, engineering research, and expert consultation, the root causes of the faults of CPSs were categorized into four factors: human factors, environmental factors, mechanical component life factors, and other factors. Using the advantages of the small sample modeling GCA method, an identification model for the influencing factors of the faults of the CPS was established. (3) Based on the actual CPS fault statistics, the correlations between the four types of faults and the four influencing factors were analyzed, and the key influencing factors of the various faults in CPSs were obtained. The research results showed that fault A of the batching metering system and fault D of the mixer were most affected by the service life factor of the mechanical parts. Fault B of the material conveying system and fault C of the pneumatic system were most affected by environmental factors. (4) The research results solve the problem of fault classification being unclear, aid with further in-depth studies on the fault influence law, and realize the identification of the key influencing factors of various faults. However, the limitation of this study lies in the lack of comparison with other methods. We will consider this in future research. (5) The real case applied in this paper was mainly used to verify the feasibility of the model. In fact, there are differences in the geographical location, climate conditions, personnel quality, CPS configuration, and organizational management level among different dam projects, and the patterns of CPS faults in different dam projects are inevitably different. However, due to the small differences in the mechanical structure characteristics and operating characteristics of CPSs, the types of production system faults and influencing factors in different projects are similar, and the identification results of key faults will vary. This is determined by the fault data obtained in a real case and has nothing to do with the model itself. Before the construction of a similar dam project, the suggestions proposed in this article can serve as a reference for engineers to make an initial maintenance plan.