# Anomaly Monitoring of Process Based on Recurrent Timeliness Rules (AMP-RTR)
## 5 Conclusions  
In this paper, based on the theory of time stamp based timeliness evaluation method, and in view of the characteristics of periodic data generation, the Recurrent Timeliness Rules (RTR) is proposed. These rules define the basis for the regularity change of periodic data timeliness; that is, the attenuation function of the timeliness evaluation value in the data update cycle and Iteration principle of timeliness evaluation value. According to RTR, the updating iteration method of timeliness evaluation value is proposed. Therefore, the Anomaly Monitoring of Process based on Recurrent Timeliness Rules (AMP-RTR) is constructed. The AMP-RTR model monitors the regularity change of periodic data timeliness to measure the possibility of abnormality in the production line. The AMP-RTR model monitors the regularity change for periodic data timeliness by adjusting the decay  
Figure 8: The convergence trend of the model considering multiple attributes.  
rate \(fall(A)\) and periodic update rate \(\alpha\), so as to meet the customized requirements for monitoring sensitivity of different production lines in the manufacturing industry.  
The robustness test of the AMP-RTR model with synthetic dataset shows that the model can monitor the abnormal situation of data bias and fluctuation. The applicability test of the AMP-RTR model with the semiconductor data set shows that the model can monitor anomalies according to the regularity change for periodic data timeliness. The semiconductor data set is divided into four parts, and the test results of temperature, humidity, flow rate and pressure on the AMP-RTR model show that the actual production line has good performance in anomaly monitoring. The analysis of the above 15 groups of simulation experiments shows that AMP-RTR model has strong applicability. The \(AARD\) method is used to measure the experimental data set used by the model, \(AARD=0.4819\). It can be seen that the degree of outlier of the data set is relatively small, indicating that the simulation experiment of the AMP-RTR model is effective.  
It is a complex and arduous task to evaluate the timeliness of periodic data and monitor the abnormal production process. In future research, the following two attempts can be made. First, in the timeliness evaluation of periodic data, because the equipment, process and environment involved in different industrial production lines are quite different, we can try to summarize the timeliness attenuation function corresponding to different scenarios. Second, we can try to set the threshold value \(N(A)\) as a dynamic change value, evaluate the timeliness of data, and monitor the abnormalities in the production process.  
Conceptualization, [PERSON]; methodology, [PERSON] and X.D.; validation, [PERSON] and J.T.; formal analysis, [PERSON] and J.T.; writing--original draft preparation, Z.L.; writing--review and editing, Z.L.; supervision, D.H. and Y.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key R&D Program of China under Grant No. 2020 YFB1707900 and 2020 YFB171800; the National Natural Science Foundation of China under Grant No. 62262074 U2268204 and 62172061; the Science and Technology Project of Sichuan Province under Grant No. 2022 YFG0159, 2022 YFG0155, 2022 YFG0157, 2021 GFW019, 2021 YFG0152, 2021 YFG0025 and 2020 YFG0322.  
Not applicable.  
It is our pleasure to acknowledge [PERSON] and [PERSON] for their assistance and support with this project.  
The authors declare no conflict of interest.