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首页> 外文期刊>Nature reviews Cancer >A Novel Semi-Supervised Feature Extraction Method and Its Application in Automotive Assembly Fault Diagnosis Based on Vision Sensor Data
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A Novel Semi-Supervised Feature Extraction Method and Its Application in Automotive Assembly Fault Diagnosis Based on Vision Sensor Data

机译：基于视觉传感器数据的新型半监督特征提取方法及其在汽车装配故障诊断中的应用

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摘要

The fault diagnosis of dimensional variation plays an essential role in the production of an automotive body. However, it is difficult to identify faults based on small labeled sample data using traditional supervised learning methods. The present study proposed a novel feature extraction method named, semi-supervised complete kernel Fisher discriminant (SS-CKFDA), and a new fault diagnosis flow for automotive assembly was introduced based on this method. SS-CKFDA is a combination of traditional complete kernel Fisher discriminant (CKFDA) and semi-supervised learning. It adjusts the Fisher criterion with the data global structure extracted from large unlabeled samples. When the number of labeled samples is small, the global structure that exists in the measured data can effectively improve the extraction effects of the projected vector. The experimental results on Tennessee Eastman Process (TEP) data demonstrated that the proposed method can improve diagnostic performance, when compared to other Fisher discriminant algorithms. Finally, the experimental results on the optical coordinate data proves that the method can be applied in the automotive assembly process, and achieve a better performance.
机译：尺寸变化的故障诊断在汽车体的生产中起重要作用。但是，难以使用传统的监督学习方法基于小标记的样本数据识别故障。本研究提出了一种名为的新型特征提取方法，半监督全内核Fisher判别（SS-CKFDA），并根据该方法引入了汽车组件的新故障诊断流程。 SS-CKFDA是传统完整的内核Fisher判别（CKFDA）和半监督学习的组合。它通过从大型未标记的样本中提取的数据全局结构调整Fisher标准。当标记样本的数量很小时，测量数据中存在的全局结构可以有效地改善投影载体的提取效应。田纳西州的伊斯坦德进程（TEP）数据的实验结果表明，与其他Fisher判别算法相比，该方法可以提高诊断性能。最后，在光学坐标数据上的实验结果证明了该方法可以应用于汽车组装过程中，实现更好的性能。

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