Concurrent control chart pattern recognition in manufacturing processes based on zero-shot learning

In real industrial settings, collecting and labeling concurrent abnormal control chart pattern (CCP) samples are challenging, thereby hindering the effectiveness of current CCP recognition (CCPR) methods. This paper introduces zero-shot learning into quality control, proposing an intelligent model for recognizing zero-shot concurrent CCPs (C-CCPs). A multiscale ordinal pattern (OP) feature considering data sequential relationship is proposed. Drawing from expert knowledge, an attribute description space (ADS) is established to infer from single CCPs to C-CCPs. An ADS is embedded between features and labels, and the attribute classifier associates the features and attributes of CCPs. Experimental results demonstrate an accuracy of 98.73 % for 11 unseen C-CCPs and an overall accuracy of 98.89 % for all 19 CCPs, without C-CCP samples in training. Compared with other features, the multiscale OP feature has the best recognition effect on unseen C-CCPs. •Proposed a zero-shot learning framework for concurrent control chart pattern recognition of manufacturing process.•Established an attribute description space to represent semantic relationships among different CCPs.•Proposed an inference model for unseen concurrent CCPs based on attribute transfer.•Established a mapping between multi-scale OP features and CCP attributes.•GZSL-CCPR model achieved a recognition accuracy of 98.89 % for up to 19 types of CCPs.