Anomaly Monitoring of Process Based on Recurrent Timeliness Rules (AMP-RTR)

At present, many manufacturing enterprises have business systems such as MES, SPC, etc. In the manufacturing process, a large amount of data with periodic time series will be generated. How to evaluate the timeliness of periodically generated data according to a large number of time series is important content in the field of data quality research. At the same time, it can solve the demand of abnormal monitoring of production process faced by manufacturing enterprises based on the regularity change for periodic data timeliness. Most of the existing data timeliness evaluation models are based on a single fixed time stamp, which is not suitable for effective evaluation of periodic data with time series. In addition, the existing data timeliness evaluation methods cannot be applied to the field of process anomaly monitoring. In this paper, the Anomaly Monitoring of Process based on Recurrent Timeliness Rules (AMP-RTR) is proposed to meet the needs of periodic data timeliness evaluation and production process anomaly monitoring. RTR is the Rules defined to evaluate the timeliness of periodically generated data. AMP is to infer the abnormality of the product production process through the abnormality of the regularity change for periodic data timeliness based on RTR. The AMP-RTR model evaluates the timeliness of data in each cycle according to the time series generated periodically. At the same time, after the updated data arrives, the initial timeliness score of the next cycle is calculated. There are two cases in which the evaluation value of timeliness is abnormal. The first case is that the timeliness score value is less than the lower limit after updating. The second case is that the number of times the timeliness score exceeds the upper limit meets the set threshold. The user can dynamically adjust the production process according to the abnormal warning of the model. Finally, in order to verify the applicability of the AMP-RTR, we conducted simulation experiments on synthetic datasets and semiconductor manufacturing datasets. The experimental results show that the AMP-RTR can effectively monitor the abnormal conditions of various production processes in the manufacturing industry by adjusting the parameters of the model.