為確保石化工廠管線流體輸送時的安全，即時偵測輸運狀態並檢查是否有異常事件的發生非常重要。及早發現管線異常，可協助運轉員盡早判斷並且採取解救措施，以免導致更大的危害。本研究提出一套石化管線監控系統，運用同一管線系統於各地所量測之流量及壓力訊號，來進行事件工況的辨識與偵測其是否為未知事件(unseen event)。在「工況辨識」方面，由於目前所獲取的數據並無運轉工況的類別標註，是以我們提出一套預標註演算法，先從原始數據萃取特徵，再利用k平均演算法將這些特徵分群並標注類別。接著，以此標註結果作為基準(ground-truth)來訓練工況辨識模型。在「未知事件檢測」方面，我們利用自動編碼器(autoencoder)的壓縮還原能力並搭配四分位距法(the interquartile range method)所訂出閾值來區分當下事件是否為未知事件。實驗結果顯示，我們所提出的石化管線監控系統在「工況辨識」上，其準確率可達99.7%。「未知事件檢測」我們先以不同運轉工況作為未知事件來進行驗證，所獲之偽陰性率(false negative rate, FNR)與偽陽性率(false positive rate, FPR)在停泵狀態時分別為0%及13.8%；而在全運轉狀態下則為0%及2.2%。最後，我們更以含有管線操作異常之數據進行驗證，所獲之未知事件檢測之FNR及FPR在全運輸狀態下分別為6.8%及3.8%。以上成果可顯示我們所提之系統對於工況辨識及未知事件檢測助益。

To ensure the safety of the fluids transporting through pipelines in the petrochemical plant, it is essential to constantly identify the working states and detect whether an abnormal event occurs. To achieve it, we proposed a petrochemical pipeline monitoring system that utilizes the flow rate and pressure signals measured at various locations for working state identification and unseen event isolation in this study. For working state identification, we first developed a pre-labeling method for automated working state labeling. Next, its results were used as the ground truth to train the state identification model. As for unseen event isolation, it was achieved by the auto-encoders (AE) of various working states and the interquartile range (IQR)-based outlier detection method. The experimental results demonstrated that the petrochemical pipeline monitoring system we proposed achieved a working state identification accuracy of 99.7%. While letting different working states take turns as unseen events for evaluating the unseen event isolators, the false negative rate (FNR) and the false positive rate (FPR) were 0% and 13.8% in the pump shut-in state, and 0% and 2.2% in the full operation state, respectively. We finally included a dataset containing abnormal events for evaluation, and the resulting FNR and FPR of the unseen event isolation were 6.8% and 3.8%, respectively, in the full operation state. All of these demonstrate the efficacy of the proposed petrochemical pipeline monitoring system.

摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vii
表目錄 ix
第一章 緒論 1
1.1 研究動機 1
1.2 文獻回顧 2
1.3 研究流程及架構 4
第二章 石化管線資料庫及前處理 6
2.1 訓練資料 6
2.2 驗證資料 6
2.2 前處理 9
2.2.1 移動平均 9
2.2.2 正規化 9
2.2.3 移動視窗 12
第三章 工況預標註 13
3.1 差分法 13
3.2 自動編碼器 16
3.3 卷積自動編碼器 16
3.3.1 卷積層 17
3.3.2 池化層 19
3.3.3 升取樣層 19
3.4 特徵萃取 20
3.5 k-平均演算法 20
第四章 石化管線運轉監控系統 22
4.1 工況辨識系統 22
4.2 未知事件檢測系統 23
第五章 實驗結果 25
5.1 預標註結果 25
5.1.1 特徵分布圖 25
5.1.2 結果圖 26
5.2 工況辨識 28
5.3 未知事件檢測 31
5.3.1 四分位距法k值訂定 32
5.3.2 未見事件檢測結果 33
5.4 系統驗證 35
5.5 架構比較 37
第六章 總結 39
6.1 結論 39
6.2 未來工作 40
6.3 特別感謝 40
參考文獻 41

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