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

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姓名

林明龍(Ming-Long Lin) 查詢紙本館藏

畢業系所

工業管理研究所

論文名稱

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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摘要(中)

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。

摘要(英)

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.

關鍵字(中)

★ 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

關鍵字(英)

★ Multivariate Processes
★ Multivariate Control Charts
★ Variable Selection
★ Deep Neural Networks
★ Model-Agnostic Meta-Learning

論文目次

摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vi
第一章、緒論 1
1.1研究背景與動機 1
1.2研究目的 1
1.3研究範圍與假設 2
1.4論文架構 2
第二章、文獻探討 4
2.1統計製程管制 4
2.1.1多變量統計製程管制 5
2.1.2 Hotelling T2管制圖 7
2.2識別異常來源相關文獻 9
2.2.1傳統統計方法 9
2.2.2機器學習方法 10
2.3主成分分析 (Principal Component Analysis, PCA) 11
2.3.1 B4-Broken-stick方法 11
2.4神經網路與深度學習 12
2.4.1神經網路 (Neural Networks, NNs) 13
2.4.2深度學習 (Deep Learning, DL) 14
2.4.3神經網路在統計製程管制上之應用 15
2.5元學習 (Meta-Learning) 16
2.5.1 Model-Agnostic Meta-Learning (MAML) 18
第三章、研究方法 19
3.1研究架構 19
3.2原始資料 20
3.3使用B4-Broken-stick方法進行變數選擇 21
3.4生成隨機多變量過程 23
3.4.1異常資料 23
3.5模型架構 24
3.5.1輸入特徵向量 24
3.5.2隱藏層 25
3.5.3輸出層 25
3.6 Model-Agnostic Meta-Learning of Deep Neural Networks 25
3.6.1問題及符號定義 26
3.6.2 MAML演算法 26
3.6.3應用於多標籤二元分類 28
第四章、實驗結果與分析 30
4.1資料前處理 30
4.2實驗設計 30
4.2.1元訓練集和元測試集之資料與樣本數 30
4.2.2最佳化模型配置 31
4.3實驗分析與評估 32
4.3.1分類結果 32
4.3.2模型比較 34
4.3.3文獻案例之模型比較 36
第五章、結論與未來研究方向 40
參考文獻 41

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指導教授

葉英傑(Ying-Chieh Yeh)

審核日期

2024-7-17

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