臺灣博碩士論文加值系統

隨著智慧製造的發展，為了即時針測設備發生異常的狀態，大量的感測器被建置於紀錄與收集生產設備相關的變數。本研究著重於預測機器剩餘壽命(Remaining Useful Life, RUL)，剩餘壽命屬於零件保養中預測性維修(Predictive Maintenance, PDM)的一環，以機器狀態為依據，根據過去機器的發展趨勢判斷機器可能損壞的時間點，目的是能提早察覺機器需要更換零件或是維修，確保系統運行的永續性。既有的文獻方法往往難以萃取出感測資料中有意義的特徵。因此，本研究提出了一個深度學習(Deep Learning)的方法，建構自動編碼門循環單元 (autoencoder gated recurrent unit, AE-GRU)神經網路， 透過自動編碼器萃取原始資料中較為重要的特徵，並利用門循環單元神經網路自行挑選序列間的資訊，準確的預測機器剩餘壽命。本研究實驗階段以IEEE國際會議2008年健康管理預診斷(Prognostics and Health Management, PHM)議題中的資料集進行模型的驗證，並以5折交叉驗證法(5 folds cross-validation)評估模型。實驗結果發現，在均方根誤差(Root Mean Squared Error)的驗證上，與其他深度學習方法deep neural network(DNN)、recurrent neural network(RNN)、long short-term memory neural network(LSTM)、gated recurrent unit neural network(GRU)比較，本研究提出的方法都優於其他方法。

With the development of smart manufacturing, in order to instantly detect abnormal conditions of the equipment, a large number of sensors were built to record the variables associated with the collection of production equipment. This research focuses on the remaining useful life(RUL)prediction. Remaining useful life is a part of the predictive maintenance(PDM), it is condition based, according to the development trend of the machine in the past to detect the machine is going to malfunction, our purpose is to detect early that the machine needs to be replaced or repaired to ensure the sustainability of the system. Existing literature methods are often difficult to extract meaningful features from sensing data. This research proposes a deep learning method, constructing an autoencoder gated recurrent unit (AE-GRU) neural network model, autoencoder extracts the important features from the raw data and gated recurrent unit picks up the information of the sequences to forecasting remaining useful life precisely. In the experiment of this research, we use for the prognostics challenge competition at the IEEE International Conference on Prognostics and Health Management (PHM08) and evaluated by 5 folds cross-validation. In the verification of root mean square error(RMSE) in our experiments, our method is better than other methods, such as deep neural network(DNN)、recurrent neural network(RNN)、long short-term memory neural network(LSTM)、gated recurrent unit neural network(GRU).

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 文獻探討 5
2.1 時間序列分析 5
2.1.1 時間序列介紹 5
2.1.2 時間序列預測應用 6
2.2 循環神經網路介紹 7
2.2.1 循環神經網路 7
2.2.2 長短期記憶神經網路 8
2.2.3 門循環單元神經網路 10
2.2.4 神經網路應用於時間序列預測 12
2.3 零件保養相關文獻 13
第三章 研究架構 19
3.1 資料前處理 21
3.1.1 定義剩餘壽命 21
3.1.2 資料標準化 22
3.1.3 特徵萃取 23
3.1.4 資料維度轉換 25
3.1.5 最大壽命值轉換 26
3.2 剩餘壽命預測模型 28
第四章 實證研究 30
4.1 資料蒐集與實驗設計 30
4.2 模型參數設定 32
4.2.1 激勵函數設定 33
4.2.2 優化器設定 34
4.2.3 隱藏層及神經元數目 35
4.3 模型評估 37
4.3.1 模型比較 37
4.3.2 剩餘壽命預測 40
4.3.2.1 剩餘壽命訓練結果 40
4.3.2.2 剩餘壽命測試結果 44
第五章 結論 48
參考文獻 50

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