Anomaly Detection of Manufacturing Process for Multi-Variety and Small Batch Production

Abstract

A series of quality control problems arise in multi-variety and small batch production due to the lack of data and other factors. In this paper, an integrated model in which control chart pattern (CCP) recognition is applied for anomaly detection is proposed to solve the problems. The integrated model is made up of four parts: feature extraction module, feature selection module, classifier module and anomaly diagnosis module. In the first module, thirteen shape features and eight statistical features of control charts are extracted. In the second module, the most representative feature set is selected by the sequential floating forward selection (SFFS) method. In the third module, a multiclass support vector machine (MSVM) which is optimized by beetle antennae search (BAS) algorithm is used to identify abnormal CCPs. In the last module, the results of pattern recognition are utilized to analyze the possible causes. The simulation results show that the CCP recognition method proposed in this paper has higher classification accuracy than other competing methods in the case of small sample with small amount of data. Finally, an example verifies that the proposed anomaly detection method is effective in multi-variety and small batch manufacturing environment.