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Concurrent control chart pattern recognition in manufacturing processes based on zero-shot learning
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DOI：10.1016/j.isatra.2024.09.001.png)
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摘要
En 中文
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 CCCPs 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.
Keyword:
Control chart pattern recognition
Concurrent CCP
Zero-shot learning
Generalized ZSL
Ordinal patterns
期刊
IF:
6.5
论文数:
6.1K
被引数:
2.0W
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引用论文
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分享收藏Mixture control chart patterns recognition using independent component analysis and support vector machine.png)
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NEUROCOMPUTINGIF6.5
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