# Research on the Reliability Allocation Method of Smart Meters Based on DEA and DBN
## 6 Conclusions  
In this paper, a smart meter reliability allocation method of multi-method fusion is proposed. First, the GO methodology is used to fuse DBN to predict the reliability. DBN can solve the complex modeling and low accuracy of the GO methodology, and the failure  
Figure 16: Abnormal display of the sample table.  
Figure 17: Reliability function diagram of the smart meter obtained by the accelerated life test.  
Figure 15: Sample meter for the accelerating life test.  
rate is based on the actual fault information of the smart meter, so this prediction method is more practical, and the result is more accurate. Second, we use the DEA method to establish a smart meter reliability allocation model. This model is a linear planning problem, so the algorithm is simple and the calculation error is small. The posterior probability obtained by the DBN backward reasoning, failure rate and structural complexity of each unit are used as the output indicators of the DEA reliability allocation model. When the DEA realizes multi-index evaluation, it does not rely on subjective evaluation, and the results are more objective. Finally, the reliability allocation results are calculated according to the result of the DEA reliability allocation model. From the allocation result presented in Section 4.2, the reliability of the smart meter in the first year is 0.99802, and the reliability of the tenth year is 0.958219. The results of the accelerated life test show that the allocation is reasonable. The allocation method is based on fault data, and the process of modeling and allocation is objective, simple and operable. Based on the actual fault information of products, this method is more practical and can improve the redistribution of reliability and the reliability of smart meters.  
This paper comprehensively considers the importance and influence of each unit on the reliability of the whole smart meter, which can be realized in technology, and the allocation method is easy to operate, which strictly ensures the overall reliability of the smart meter. This paper also has some limitations: The dynamic Bayesian network based on the GO diagram is a network with a large number of nodes and a complex relationship; in the iterative computation, the problems such as the termination of the path, the speed of computation, and the number of cycles will all become apparent, and it will be necessary to decompose the dynamic Bayesian network. In the future, according to the characteristics of the GO methodology, it is necessary to establish the segmentation criterion of the GO diagram and decompose it into several independent subgraphs.  
Data curation, Z.W.; formal analysis, J.Z.; investigation, Z.W.; methodology, J.Z. and Z.Y.; software, Z.W.; supervision, Z.Y.; writing-original draft, J.Z. All authors have read and agreed to the published version of the manuscript.  
This project is supported by Basic public welfare research project of Zhejiang province (Grant No. LGG18E050008), the National Natural Science Foundation of China (Grant No. 51675481).  
Not applicable.  
The data presented in this study are available on request from corresponding author.  
The authors declare no conflict of interest.