Online Monitoring of Manufacturing Process Based on autoCEP

Abstract

Complex Event Processing (CEP), which can identify patterns of interest from a large number of continuous data steam, is becoming more and more popular in manufacturing process monitoring. CEP rules are specified manually by domain expert, which is a limiting factor for its application in manufacturing enterprises. How to analysis historical data and automatically generate CEP rules is becoming a challenge research. This paper proposed a model of autoCEP for online monitoring in product manufacturing, which can automatically generate CEP rules based on association rules mining in key processes. First, the key quality factors in manufacturing process were extracted by grey entropy correlation analysis. Then, association rules mining method based on product process constraints was used to find the association rules between key factors and product quality. At last, the extracted rules are algorithmically transformed into CEP rules. The experimental results show the effectiveness and practicability of the proposed method.