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應用系統模擬於真空電鍍廠之設備能源效率評估 = = Applicatio...

杜祈毅

應用系統模擬於真空電鍍廠之設備能源效率評估 = = Application of system simulation to evaluate Overall Equipment Energy Effectiveness(OEEE) in Physical Vapor Deposition factory /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	應用系統模擬於真空電鍍廠之設備能源效率評估 =/ 杜祈毅.
其他題名:	Application of system simulation to evaluate Overall Equipment Energy Effectiveness(OEEE) in Physical Vapor Deposition factory /
其他題名:	Application of system simulation to evaluate Overall Equipment Energy Effectiveness(OEEE) in Physical Vapor Deposition factory.
作者:	杜祈毅
出版者:	雲林縣 :國立虎尾科技大學 , : 民113.05.,
面頁冊數:	[9], 70面 :圖, 表 ; : 30公分.;
附註:	指導教授: 李孟樺.
標題:	時間序列分析. -
電子資源:	電子資源

應用系統模擬於真空電鍍廠之設備能源效率評估 = = Application of system simulation to evaluate Overall Equipment Energy Effectiveness(OEEE) in Physical Vapor Deposition factory /
杜祈毅

應用系統模擬於真空電鍍廠之設備能源效率評估 =Application of system simulation to evaluate Overall Equipment Energy Effectiveness(OEEE) in Physical Vapor Deposition factory /Application of system simulation to evaluate Overall Equipment Energy Effectiveness(OEEE) in Physical Vapor Deposition factory.杜祈毅. - 初版. - 雲林縣 :國立虎尾科技大學 ,民113.05. - [9], 70面 :圖, 表 ;30公分.

指導教授: 李孟樺.

碩士論文--國立虎尾科技大學工業管理系工業工程與管理碩士班.

含參考書目.

產業及環境快速變化，全球企業開始積極推動綠色永續製造，ESG成為了關鍵趨勢；台灣中小型產業占比達98%之高，且金屬產業又為台灣又為重點產業之一，面對轉型挑戰，不少中小金屬企業舉步維艱。本研究以台灣中部某金屬表面處理廠家作為案例，探討精實管理與綠色永續的實踐予實現整體設備效率評估之預測。　　研究核心已先行針對合作個案企業之作業現場浪費實施精實智慧製造相關改善，透過價值溪流圖(Value Stream Mapping, VSM)進行診斷並實施5S、lay-out與目視化管理等手法介入後，現階段藉由鍍膜機台之用電情形進行資料蒐集，並利用系統模擬執行設備能源效率評價預測；研究方法藉由資訊看板取得相關數據並進行資訊標準化，提升廠區資訊的可靠性；並利用簡單移動平均(Simple Moving Average, SMA)、加權移動平均(Weighted Moving Average, WMA)、自迴歸移動平均模型(Autoregressive Integrated Moving Average model, ARIMA)、季節性自迴歸移動平均模型(Seasonal Autoregressive Integrated Moving Average Model, SARIMA)及長短記憶網路(Long Short-Term Memory, LSTM)等方法進行時間序列分析。　　結果顯示，透過實施超參數優化(Hyperparameter Optimization)與早停法(Early stopping)改良後的LSTM模型表現最為顯著，在平均絕對百分比誤差(Mean Absolute Percentage Error, MAPE)及平均絕對誤差(Mean Absolute Error, MAE)之績效上皆以證明該為最佳模型，均方誤差(Mean-Square Error, MSE)則與ARIMA模型表現結果相近，本研究不僅提供能源效率評價的具體框架，還為企業提供了實現淨零排放且可應對產業發展趨勢之有效途徑作為參考。.

(平裝)Subjects--Topical Terms:

1449100
時間序列分析.

應用系統模擬於真空電鍍廠之設備能源效率評估 = = Application of system simulation to evaluate Overall Equipment Energy Effectiveness(OEEE) in Physical Vapor Deposition factory /
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246 1 1 $a Application of system simulation to evaluate Overall Equipment Energy Effectiveness(OEEE) in Physical Vapor Deposition factory.
250 $a 初版.
260 # $a 雲林縣 : $b 國立虎尾科技大學 , $c 民113.05.
300 $a [9], 70面 : $b 圖, 表 ; $c 30公分.
500 $a 指導教授: 李孟樺.
500 $a 學年度: 112.
502 $a 碩士論文--國立虎尾科技大學工業管理系工業工程與管理碩士班.
504 $a 含參考書目.
520 3 $a 產業及環境快速變化，全球企業開始積極推動綠色永續製造，ESG成為了關鍵趨勢；台灣中小型產業占比達98%之高，且金屬產業又為台灣又為重點產業之一，面對轉型挑戰，不少中小金屬企業舉步維艱。本研究以台灣中部某金屬表面處理廠家作為案例，探討精實管理與綠色永續的實踐予實現整體設備效率評估之預測。　　研究核心已先行針對合作個案企業之作業現場浪費實施精實智慧製造相關改善，透過價值溪流圖(Value Stream Mapping, VSM)進行診斷並實施5S、lay-out與目視化管理等手法介入後，現階段藉由鍍膜機台之用電情形進行資料蒐集，並利用系統模擬執行設備能源效率評價預測；研究方法藉由資訊看板取得相關數據並進行資訊標準化，提升廠區資訊的可靠性；並利用簡單移動平均(Simple Moving Average, SMA)、加權移動平均(Weighted Moving Average, WMA)、自迴歸移動平均模型(Autoregressive Integrated Moving Average model, ARIMA)、季節性自迴歸移動平均模型(Seasonal Autoregressive Integrated Moving Average Model, SARIMA)及長短記憶網路(Long Short-Term Memory, LSTM)等方法進行時間序列分析。　　結果顯示，透過實施超參數優化(Hyperparameter Optimization)與早停法(Early stopping)改良後的LSTM模型表現最為顯著，在平均絕對百分比誤差(Mean Absolute Percentage Error, MAPE)及平均絕對誤差(Mean Absolute Error, MAE)之績效上皆以證明該為最佳模型，均方誤差(Mean-Square Error, MSE)則與ARIMA模型表現結果相近，本研究不僅提供能源效率評價的具體框架，還為企業提供了實現淨零排放且可應對產業發展趨勢之有效途徑作為參考。.
520 3 $a As the industry and environment change rapidly, global enterprises have begun to actively promote green sustainable manufacturing, at the same time, ESG has become a key trend. Taiwan's Small and Medium-sized Enterprises(SMEs) account for 98%, and the metal industry is one of the key industries in Taiwan. However, in the face of the challenge of transformation, many small and medium-sized metal enterprises are struggling. This research takes a metal surface treatment plant in central Taiwan as an example to explore the application of lean management and green sustainable practices in Overall Equipment Efficiency Evaluation(OEEE) prediction. 　　The core of the research had been implemented lean and smart manufacturing relevantly improvements on the worksite waste of cooperative factory, using Value Stream Mapping (VSM) to diagnose promoting implement 5S, Lay-Out, Visual Management and other related improvements. Now, the project collects data through the power consumption of coating machines, and system simulation is used to perform equipment energy efficiency evaluation and prediction. The research method first obtains relevant data through information boards and standardizes the information to improve the reliability of the information in the factory area. Then, Simple Moving Average(SMA), Weighted Moving Average(WMA), Autoregressive Integrated Moving Average model(ARIMA), Seasonal Autoregressive Integrated Moving Average model(SARIMA), and Long Short-Term Memory(LSTM) are used for time series analysis. 　　The results show that adopt Hyperparameter Optimization and Early stopping cause LSTM model exhibits the most remarkable performance, outperforming other models in terms of Mean Absolute Percentage Error(MAPE) and Mean Absolute Error(MAE). The Mean Square Error(MSE) is comparable to the performance of the ARIMA model. This research not only provides a concrete framework for energy efficiency evaluation but also offers valuable insights for enterprises to achieve net-zero emissions and respond to industry development trends..
563 $a (平裝)
650 # 4 $a 時間序列分析. $3 1449100
650 # 4 $a 綠色精實生產. $3 1449101
650 # 4 $a 設備能源效率評價. $3 1449102
650 # 4 $a 系統模擬. $3 995653
650 # 4 $a Time series analysis. $3 1449103
650 # 4 $a Green Lean Production. $3 1449104
650 # 4 $a Overall Equipment Energy Effectiveness. $3 1449105
650 # 4 $a System Simulation. $3 1153638
856 7 # $u https://handle.ncl.edu.tw/11296/2uyy3p $z 電子資源 $2 http