基於Model-Agnostic Meta-Learning方法的深度神經網路識別多變量製程中的異常來源;Identifying the Sources of Out-of-control Signals in Multivariate Process Using Deep Neural Networks Based on Model-Agnostic Meta-Learning Approach

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题名:	基於Model-Agnostic Meta-Learning方法的深度神經網路識別多變量製程中的異常來源;Identifying the Sources of Out-of-control Signals in Multivariate Process Using Deep Neural Networks Based on Model-Agnostic Meta-Learning Approach
作者:	林明龍;Lin, Ming-Long
贡献者:	工業管理研究所
关键词:	多變量製程;多變量管制圖;變數選擇;深度神經網路;Model-Agnostic Meta-Learning;Multivariate Processes;Multivariate Control Charts;Variable Selection;Deep Neural Networks;Model-Agnostic Meta-Learning
日期:	2024-07-17
上传时间:	2024-10-09 15:16:26 (UTC+8)
出版者:	國立中央大學
摘要:	在現代製造業中，多變量管制圖已被廣泛應用於檢測製程異常。然而，它卻無法直接辨識異常來源，這是製程監控中的一大挑戰。因此，本研究旨在發展一套有效且即時的方法，以應對製造過程中的維度災難，同時識別多變量製程中的異常來源，提高監控的準確性和效率。資料前處理採用B4-Broken-stick變數選擇方法，有效降低多維度數據的複雜性。本研究使用基於Model-Agnostic Meta-Learning方法的深度神經網路進行異常來源的分類。實驗結果顯示，本研究提出的 MAML of DNNs 模型透過少量樣本學習，快速適應新任務，在不同偏移情況下能夠有效處理所有異常狀況並保持良好的準確率。與 MLP 模型相比，MAML of DNNs 模型在準確率和訓練時間上均表現較佳，證實其在識別多變量製程異常來源方面更具效率和效果。將MAML of DNNs模型應用於生產過程中，可以即時辨識異常來源，幫助相關領域人員更有效率和高效地進行品質改善工作。;In modern manufacturing, multivariate control charts have been widely used to detect process anomalies. However, they cannot directly identify the source of out-of-control signals, posing a significant challenge in process monitoring. This study aims to develop an effective and real-time method to address the curse of dimensionality in manufacturing processes while identifying the source of out-of-control signals in multivariate processes, thereby improving monitoring accuracy and efficiency. This study employs the B4-Broken-stick variable selection method to effectively reduce the complexity of high-dimensional data. We propose using a deep neural network based on Model-Agnostic Meta-Learning (MAML) to classify the sources of out-of-control signals. Experimental results show that the proposed MAML of DNNs model learns from a small number of samples and quickly adapts to new tasks, effectively handling all anomaly conditions and maintaining high accuracy under various shift scenarios. Compared to the MLP model, the MAML of DNNs model performs better in both accuracy and training time, demonstrating its efficiency and effectiveness in identifying the sources of out-of-control signals in multivariate processes. Applying the MAML of DNNs model in the production process enables real-time identification of the sources of out-of-control signals, assisting personnel in related fields to conduct quality improvement work more efficiently and effectively.
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