基於門控循環單元於塗佈機異常偵測之研究 —以A公司為例

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姓名

李珮榆(Pei-Yu Li) 查詢紙本館藏

畢業系所

工業管理研究所

論文名稱

基於門控循環單元於塗佈機異常偵測之研究 —以A公司為例
(Anomaly Detection for Coating Machine Based on Gated Recurrent Unit Approach —A Case Study of Company A)

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摘要(中)

隨著工業 4.0 的時代來臨，智慧製造成為近年來最熱門的產業趨勢與競爭決勝關鍵，世界各大廠商均紛紛導入智慧製造相關技術，臺灣各工具機大廠也整合人工智慧 (Artificial Intelligence, AI)、感測器 (Sensor) 及物聯網 (Internet of Things, IoT) 等技術，將設備轉型為智慧機械，發展成當今全球市場樣態，而預測性維護技術在智慧製造中扮演非常重要的角色，會將機器設備加裝的感測器所收集並儲存於雲端系統的即時資料進行分析，預測未來機台故障的可能性與時間點，避免停機問題的發生，進而提升整體設備效率。
本研究使用 A 公司塗佈機實際生產時所回傳的資料，提出一項基於門控循環單元 (Gated Recurrent Unit, GRU) 的異常檢測方法，對設備進行診斷，先將資料透過主成分分析 (Principal Components Analysis, PCA) 進行降維，取出重要特徵後再透過 GRU 來建置預測模型，最後將驗證結果繪製出來，可以觀察約能在 30.5 秒前偵測到異常，促使維修人員在設備故障前進行維護程序，預先解決潛在問題，進而提升設備正常運作時間與生產品質。

摘要(英)

With the advent of Industry 4.0, smart manufacturing has become the most popular industrial trend and the key to competition in recent years. The world′s leading manufacturers have imported into technologies related to smart manufacturing, and Taiwan′s major tool manufacturers have also integrated Artificial Intelligence (AI), Sensor and Internet of Things (IoT) technologies to transform their equipment into smart machines, which have developed into the current global market pattern. The technology of predictive maintenance plays a very important role in the smart manufacturing. Through the analysis of the real-time data collected by the sensors installed in the machine equipment and stored in the cloud system, the possibility and timing of future machine failure can be predicted to avoid the occurrence of machine downtime. This will prevent downtime problems and improve overall equipment efficiency.
In this study, we propose an anomaly detection method based on Gated Recurrent Unit (GRU) to diagnose the equipment by using the data returned from the actual production of coating machines in Company A. The data is reduced dimension through Principal Components Analysis (PCA) to extract important features and then GRU is used to build a predictive model. Finally, after plotting the validation results, you can observe that anomalies can be detected about 30.5 seconds earlier. To prompt service person to carry out maintenance procedures before equipment failure and solve potential problems in advance, thus improving equipment operation time and production quality.

關鍵字(中)

★ 深度學習
★ 主成分分析
★ 門控循環單元
★ 機台異常預診斷
★ 智慧製造

關鍵字(英)

★ Deep Learning
★ Principal Components Analysis
★ Gated Recurrent Unit
★ Anomaly Prognostics of Machine
★ Smart Manufacturing

論文目次

中文摘要...I
Abstract...II
目錄...III
圖目錄...V
表目錄...VII

一、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

二、文獻探討...4
2-1故障預測與健康管理(Prognostic and Health Management, PHM)...4
2-2深度學習(Deep Learning, DL)...6
2-3門控循環單元 (Gated Recurrent Unit, GRU)...9
2-4主成分分析 (Principal Components Analysis , PCA)...11

三、研究方法...13
3-1研究項目...13
3-2研究問題...14
3-3主成分分析(PCA)...15
3-4門控循環單元(GRU)...18
3-4-1滑動窗口 (Sliding Window)...22
3-4-2激勵函數(Activation Function)...22
3-4-3損失函數 (Loss Function)...25
3-4-4優化器 (Optimizer)...25
3-5模型評估(Model Evaluation)...26

四、實驗與分析...28
4-1實驗環境與開發工具...28
4-2實驗流程...29
4-2-1實驗資料說明...29
4-2-2資料降維...31
4-2-3實驗設計...33
4-3實驗結果...37
4-3-1驗證資料...37
4-3-2驗證結果...38

五、結論與未來研究方向...42
5-1結論...42
5-2未來研究方向...43
參考文獻...44

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指導教授

陳振明(Jen-Ming Chen)

審核日期

2022-7-5

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