臺灣博碩士論文加值系統

為了即時偵測設備發生狀態異常或故障，大量的感測器被建置以及時紀錄與收集生產設備相關的變數，如溫度、壓力等。收集的設備感測資料為一組依照時間先後順序紀錄而得的資料，又可稱為時間性資料(time-series data)，且具有大量與快速累積的特性。然而，設備感測資料分佈往往無法僅用特定分配來描述，使得既有的文獻方法往往難以自動萃取感測資料中有意義的特徵，為了同時考慮設備異常偵測與診斷，因此，本研究基於深度學習(deep learning)模型的特徵學習優點，建構錯誤偵測與分類的深度卷積神經網路(Fault Detection Classification-Deep Convolution Neural Network, FDC-DCNN)模型，針對設備感測資料在考慮原始時間性的變化下學習感測器之間的特徵，並建構異常偵測與診斷網路架構，得以在異常偵測與分類的同時，也提供工程師在診斷上快速排除異常的資訊。本研究以某半導體製程中的設備感測資料驗證模式效度，實驗結果發現本研究模型在5折交叉驗證下，在準確率、召回率和正確率均優於既有文獻方法。所提出FDC-DCNN透過診斷層的輸出值找出每一個感測器之間的關聯，解決既有CNN在異常診斷上資訊不足的困難。

The development of computing technology and process technology have been enhanced the rapid changes in high-tech products and smart manufacturing, specifications become more sophisticated. Large amount of sensors are installed to record equipment condition during the manufacturing process. In particular, the characteristics of sensor data are temporal. Most the existing time series analysis approaches cannot extract the effective feature from a large number of sensor data, find that the relationship between each sensor, and accurately detect the fault. This study aims to construct a fault detection classification-deep convolution neural network (FDC-DCNN) model for fault detection and diagnosis. This study also incorporate data augmentation to enhance the diversity and also avoid over-fitting. The key features of equipment sensor can be learned automatically through CNN model. Find the important of each sensor through the diagnostic layer in the model architecture. To validate the proposed FDC-DCNN, an empirical study from a wafer fabrication was conducted. Based on the 5-folds cross-validation, the experimental results show that the model can accurately detect the abnormality with high accuracy rate, recall and precision, and outperform than other existing methods. Through the output value of the diagnostic layer in FDC-DCNN, we can identify the relationship between each fault and different sensors.

書名頁 i
論文口試委員審定書 ii
授權書 iii
中文摘要 iv
英文摘要 v
誌謝 vi
目錄 vii
表目錄 ix
圖目錄 x
第一章 緒論 1
1.1 研究背景與重要性 1
1.2 研究動機 2
1.3 研究目的 3
1.4 研究架構 4
第二章 文獻回顧 5
2.1 時間序列分析 5
2.1.1 時間序列的相似度 5
2.1.2 時間序列的特徵萃取 6
2.2 多元時間序列分析 8
第三章 研究架構 11
3.1 資料前處理 14
3.1.1 資料標準化 14
3.1.2 資料擴增 15
3.2異常偵測與診斷 16
3.2.2 異常偵測 18
3.2.3 診斷層 20
第四章 實證研究 21
4.1 資料蒐集與實驗設計 21
4.2 FDC感測資料前處理 22
4.3 模型參數設定 24
4.3.1 特徵萃取 24
4.3.2異常偵測與分類 26
4.4 FDC異常偵測 27
第五章 結論 33
參考文獻 35

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