Abstract Profile monitoring remains an interesting issue in statistical process control (SPC). Although there have been considerable researches devoted to analysis of profile data, the challenges concerning the monitoring of complex profiles (e.g., multivariate profiles, nonlinear profile, autocorrelated profiles) is yet to be addressed well. The high-dimension explanatory variables and autocorrelation generally affect effectiveness of those regular profile monitoring models and could cause many false alarms. Recent years have witnessed remarkable successes of deep learning techniques in visual and acoustic studying fields. In this paper, a deep learning model known as stacked denoising autoencoders (SDAE) is developed for complex profiles modeling and monitoring. Three control charts based on the SDAE model are further developed for abnormal detection of complex profiles. Comparison between the proposed method and other typical methods is implemented to illustrate effectiveness of the proposed method in five representative profiles. Finally, a real dataset is further utilized to demonstrate the effectiveness of the proposed method in agriculture fields. The experimental results illustrate the effectiveness of the SDAE-based method on complex profiles monitoring. This paper provides an inspiration for using deep learning techniques to monitor complex profiles.

中文翻译：

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基于深度堆叠去噪自编码器的复杂轮廓监测

摘要 轮廓监控仍然是统计过程控制 (SPC) 中的一个有趣问题。尽管已经有大量研究致力于剖面数据的分析，但有关复杂剖面（例如，多变量剖面、非线性剖面、自相关剖面）的监测的挑战尚未得到很好的解决。高维解释变量和自相关通常会影响那些常规剖面监测模型的有效性，并可能导致许多误报。近年来，深度学习技术在视觉和声学研究领域取得了显着的成功。在本文中，开发了一种称为堆叠降噪自编码器 (SDAE) 的深度学习模型，用于复杂的轮廓建模和监控。进一步开发了基于 SDAE 模型的三个控制图，用于复杂轮廓的异常检测。将所提出的方法与其他典型方法进行比较，以说明所提出方法在五个代表性配置文件中的有效性。最后，进一步利用真实数据集来证明所提出的方法在农业领域的有效性。实验结果说明了基于 SDAE 的方法对复杂剖面监测的有效性。本文为使用深度学习技术监控复杂配置文件提供了灵感。进一步利用真实数据集来证明所提出的方法在农业领域的有效性。实验结果说明了基于 SDAE 的方法对复杂剖面监测的有效性。本文为使用深度学习技术监控复杂配置文件提供了灵感。进一步利用真实数据集来证明所提出的方法在农业领域的有效性。实验结果说明了基于 SDAE 的方法对复杂剖面监测的有效性。本文为使用深度学习技术监控复杂配置文件提供了灵感。