臺灣博碩士論文加值系統

人工智慧、大數據等技術日漸提升，使得製造業開始倡導智慧工廠，因此設備上的安全、效率及可靠度格外重要，在設備上的預測性維護是非常關鍵的一環。預測性維護技術涉及了如何能準確預估設備剩餘使用壽命，提前預測設備是否故障。過去有不少文獻提到，預測性維護技術來預估設備剩餘使用壽命不但可以降低維護成本，並可以藉此延長機器設備的實際使用時間。因此，提升預測性維護並預估設備剩餘使用壽命的準確性，是一項值得深入探討的議題。本研究針對機械設備剩餘使用壽命的預測提出一個基於深度學習之混合方法，此方法結合卷積神經網路(CNN)、長短期記憶網路(LSTM)、雙向長短期記憶網路(BiLSTM)，不但能更好地提取重要特徵，還能處理長時間序列問題，並有效的學習前向與後向數據的依賴性。與過往研究方法之不同處在於，本研究混合了更多不同的深度學習方法(CNN、LSTM、BiLSTM)，針對其不同的優點善加利用。本研究實驗階段中，以渦扇發動機數據集和軸承數據集，兩個開放資料集進行模型驗證，與先前的研究相比，結果顯示所提出的方法在RMSE有良好的結果，並優於其他方法。

Artificial intelligence, big data and other technologies are improving, making the manufacturing industry to advocate smart factories, so the safety, efficiency and reliability of the equipment is particularly important, predictive maintenance on the equipment is a very critical part. Predictive maintenance technology involves how to accurately predict the remaining useful life of equipment and whether the equipment will fail in advance, and it has been mentioned in the literature in the past that predictive maintenance technology to predict the remaining useful life of equipment can not only reduce maintenance costs, but also extend the actual useful life of machines and equipment. In this study, a hybrid method based on deep learning is proposed for predicting the remaining useful life of machinery and equipment. This method combined with CNN, LSTM and BiLSTM can not only better extract important features, but also deal with long sequence problems, and effectively learn the dependence of forward and backward data. What distinguishes this study from previous research methods is that it mixes more different deep learning methods and makes good use of their different strengths. In the experimental phase of this study, the model was validated with two open datasets and the results will be compared with previous studies. The comparative results show that the proposed method has good results in RMSE and outperforms other methods, improving the predictive power in this regard.

書名頁 i
論文口試委員審定書 ii
中文摘要 iii
英文摘要 iv
誌謝 v
目錄 vi
表目錄 viii
圖目錄 ix
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 4
1.3 研究流程 5
第二章 文獻探討 7
2.1 預測性維護 7
2.2 剩餘使用壽命 10
2.3 深度學習 13
2.3.1 卷積神經網路 14
2.3.2 長短期記憶網路 19
第三章 研究方法 24
3.1 研究架構 24
3.2 研究流程 26
3.3 研究資料 28
3.3.1 渦扇發動機退化仿真數據集(Turbofan Engine Degradation Simulation Data Set) 28
3.3.2 FEMTO軸承數據集(FEMTO Bearing Data Set) 29
3.3.3 資料處理 30
3.4 混合深度學習方法模型建構 32
3.4.1 預測模型建構 32
3.4.2 激勵函數(Activation Function) 34
3.4.3 目標函數(Object Function) 35
3.5 模型績效衡量指標 35
3.6 實驗環境 36
第四章 研究結果 37
4.1 實驗結果 37
4.1.1 Turbofan Engine Degradation Simulation Data Set 37
4.1.2 FEMTO Bearing Data Set 43
4.2 小結 46
第五章 結論與建議 47
5.1 結論 47
5.2 建議和未來研究方向 48
參考文獻 49

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