應用LSTM-OCSVM模型於塗佈機異常偵測之研究

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姓名

鄭博安(Po-An Cheng) 查詢紙本館藏

畢業系所

工業管理研究所

論文名稱

應用LSTM-OCSVM模型於塗佈機異常偵測之研究
(Applying LSTM-OCSVM Model for Anomaly Detection in Coating Machine)

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摘要(中)

隨著現代科技的進步，全世界的各大企業正逐步地往工業4.0前進，伴隨著人工智慧這項技術已逐漸成熟，不論是製造業、金融業、能源業等不同領域，皆為了穩定高效的生產效率、提高產品良率及機台的低故障發生率，紛紛投入大量的研發費為了導入智慧製造相關的技術。
本研究運用A公司提供的塗佈機運轉數據，採納了長短期記憶網路（ Long Short-Term Memory, LSTM ）和單類支持向量機（ One-Class Support Vector Machines, OCSVM ）兩種方法的混合模型，以半監督式學習的方式建立，旨在實現預測性維護方法中的預診斷與健康管理（ Prognostics and Health Management, PHM ）。準確度為85.83%，精確度為46.82%，召回率為98.97%，F2-Score為80.9%，表明LSTM-OCSVM方法在異常偵測上的優越性。且預測模型相較於實際張力異常紀錄能提前約500秒檢測到異常徵兆，以利機台負責人員能於故障前做出決策，確保產線順暢，並降低因機台異常導致產品良率下降及機台壽命減短等問題。

摘要(英)

As modern technology advances, major companies worldwide are gradually moving towards Industry 4.0. Along with the gradual maturation of artificial intelligence technology, various sectors such as manufacturing, finance, and energy are all investing heavily in research and development to implement smart manufacturing technologies. This is aimed at achieving stable and efficient production, improving product quality, and reducing machine failure rates.
This study uses operational data from coating machines provided by Company A, adopting a hybrid model of Long Short-Term Memory (LSTM) and One-Class Support Vector Machines (OCSVM) methods, established in a semi-supervised learning manner. The aim is to achieve prognostics and health management (PHM) in predictive maintenance methods. The accuracy is 85.83%, precision is 46.82%, recall is 98.97%, and the F2-Score is 80.9%, indicating the superiority of the LSTM-OCSVM method in anomaly detection. The predictive model can detect abnormal signs about 500 seconds earlier compared to actual tension abnormal records, allowing machine operators to make decisions before a failure occurs, ensuring smooth production lines, and reducing issues such as decreased product yield and shortened machine lifespan caused by machine anomalies.

關鍵字(中)

★ 智慧製造
★ 長短期記憶網路
★ 單類支持向量機
★ 預測性維護

關鍵字(英)

★ Smart Manufacturing
★ Long Short-Term Memory Network
★ One-Class Support Vector Machine
★ Predictive Maintenance

論文目次

目錄
摘要 I
ABSTRACT II
目錄 III
圖目錄 VI
表目錄 IX
第一章緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究架構 3
第二章文獻回顧 4
2.1 虛實整合系統 ( CYBER PHYSICAL SYSTEM, CPS ) 4
2.2 機器學習 ( MACHINE LEARNING, ML ) 5
2.3 深度學習( DEEP LEARNING, DL) 8
2.4 預測性維護策略( PREDICTIVE MAINTENANCE, PDM ) 11
2.4.1 預診斷與健康管理架構 12
2.4.2 預測性維護的預測方法 13
第三章研究方法 16
3.1 研究對象 16
3.2 問題定義 18
3.3 長短期記憶法（LONG SHORT-TERM MEMORY） 20
3.3.1激勵函數 ( Activation Function ) 22
3.3.2 損失函數( Loss Function ) 25
3.3.3 優化器 (Optimizer) 26
3.4 支撐向量機( SUPPORT VECTOR MACHINE, SVM) 27
3.4.1 核函數 ( Kernel Function ) 30
3.4.2 單類支撐向量機( One- Class SVM, OCSVM ) 31
3.5 評價指標 ( EVALUATION METRICS ) 33
第四章實驗結果與分析 35
4.1 資料說明及預處理 35
4.2實驗設計 37
4.2.1滑動窗口 ( Sliding window) 37
4.2.2神經元數量 39
4.2.3模型建置 42
4.2.4懲罰係數與核函數 43
4.3實驗結果 44
4.4實驗分析與評估 48
4.4.1健康管理機制 49
4.4.2 模型效益比較 52
第五章結論與未來建議 54
5.1 結論 54
5.2 未來建議 56
參考文獻 58

參考文獻

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指導教授

陳振明(Jen-Ming Chen)

審核日期

2024-7-1

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