Statistical Methods and Applications for Anomaly Detection in the Production Process
This study aims to explore and evaluate various statistical methods and their applications for anomaly detection in the production process.With the rapid development of industrial automation and intelligent manufacturing,ensuring the stability of the production process and product quality has become particularly important.We applied a series of statistical analysis and machine learning techniques,including descriptive statistical analysis,control charts(Shewhart chart,CUSUM chart,EWMA chart),principal component analysis(PCA),and clustering analysis,to identify and handle abnormal situations in the production process of urea by analyzing a case study of the anomaly detection strategy implemented by Hualu Hengsheng Chemical Co.,Ltd.Research has found that the comprehensive application of these methods can effectively monitor production processes,detect anomalies in a timely manner,and thereby improve production efficiency and product quality.In addition,we also discussed the advantages and limitations of each method and proposed suggestions for anomaly detection in the production process.Finally,future research directions were explored,including the potential of deep learning in anomaly detection and the fusion of cross domain methods.
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