# Anomaly Monitoring of Process Based on Recurrent Timeliness Rules (AMP-RTR)
### Paper Organization and Contribution  
The guiding principles of designing scientific research defined in reference [6], we regard the recurrent timeliness Rules as our artifact, and organize the paper as follows: after discussing the relevance of the problems in this introduction, Section 2 briefly summarizes the relevant work on data timeliness, and identifies the research gap. Our contribution is to propose the Anomaly Monitoring of Process based on Recurrent Timeliness Rules, which infers the abnormality of the production process according to the abnormality of the timeliness score. In Section 3, based on the previous work and the actual needs of manufacturing production, we first summarized the requirements for Recurrent Timeliness Rules. Then, the evaluation method of the timeliness of periodic data and the realization of the process anomaly monitoring model are introduced, respectively. In Section 4, we first tested the robustness of the model on synthetic datasets, and then carried out simulation experiments on the semiconductor manufacturing datasets. In Section 5, we summarize the advantages of this model over the existing methods and look forward to the research points that can be optimized in the future.  
Figure 1 summarizes the ideas of this research work. The AMP-RTR research flow chart divides the research content of this paper into two main aspects: one is the iterative process for the timeliness evaluation of periodic data (i.e., RTR), and the other is the implementation process of anomaly monitoring (i.e., AMP). This figure clearly shows how  
the timeliness evaluation achieves iterative updating, and how to realize the abnormal monitoring and feedback of the production line.  
The possible contributions of this paper are as follows:  
1. The current data timeliness evaluation method is based on a single timestamp in the database and cannot evaluate the timeliness of periodic data with time series. Therefore, according to the characteristics of periodic production of manufacturing data, we propose the Recurrent Timeliness Rules (RTR). RTR define the basis for regularity change of periodic data timeliness; that is, how the regularity change of periodic data timeliness occurs during the update cycle, and how the regularity change of periodic data timeliness is updated iteratively. According to RTR's constraints on the regularity change for periodic data timeliness, the timeliness evaluation of periodic data can be realized.
2. According to the regularity change for periodic data timeliness, we propose the Anomaly Monitoring of Process based on Recurrent Timeliness Rules (AMP-RTR). At the same time, we specified two cases of abnormal changes in the regularity change for periodic data timeliness: the first abnormal state is that the timeliness evaluation value is lower than the lower limit after updating; the second abnormal state is that the number of times the timeliness evaluation value is higher than the upper limit reaches the set threshold after being updated. AMP-RTR model monitors whether the update iteration of regularity change of periodic data timeliness is abnormal, so as to calculate the possibility of abnormality in the production line. The model adjusts the decay 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.