使用長短期記憶深度學習 之機器剩餘可用壽命預估

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姓名

許哲昇(Che-Sheng Hsu) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

使用長短期記憶深度學習之機器剩餘可用壽命預估
(Long Short-Term Memory Deep Learning for Estimating Machinery Remaining Useful Life)

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摘要(中)

近年來，發展智慧工廠成為全球製造業的目標，結合了人工智慧、物聯網、大數據和雲端運算等先進技術，希望以機器人、智慧製造和智慧服務等面向做到「智動化」，有助於提升生產的效率和商品的良率，並解決人力短缺、市場需求變化大的問題。本篇論文著重於預測機器剩餘可用壽命(Remaining Useful Life, RUL)，屬於機器健康預診斷(Prognosis)的應用，可用於預估機器剩餘可用壽命，在機器停止運作前，提早進行維修或更換，以降低機器突然停機所造成的損害，提高系統的運行可靠性。
本論文提出一個深度學習(Deep Learning)的方法，建構深度神經網路(Deep Neural Network)以預估機器剩餘可用壽命。所提之方法基於時間遞歸神經網路(Recurrent Neural Network, RNN)中的長短期記憶(Long Short-Term Memory, LSTM)模式。LSTM比傳統RNN更適合於處理和預測時間序列中間隔和延遲非常長的重要相關資訊，可以有效找出時間序列中的間隔相當長的相關資訊特徵。我們希望使用LSTM長記憶的特性，準確預估機器剩餘可用壽命。
為了驗證所提方法的效能，以NASA C-MAPSS(Commercial Modular Aero-Propulsion System Simulation)包含二百多組引擎模擬資料的資料集做驗證，並且與文獻中的MLP、SVR、RVR和CNN方法做比較。結果顯示，無論是在均方根差(Root Mean Squared Error, RMSE)還是在資料集本身定義的Scoring Function上，所提的方法都是最佳的。本論文最後並提出實作上的觀察和所提方法未來可能的應用場景。

摘要(英)

In recent years, it is a worldwide goal to develop smart factories by integrating the artificial intelligence, Internet of Things and cloud computing technologies. Smart factories can achieve higher yield rates and better quality; they can also mitigate the problems of labor shortage and react properly to the dynamically changing of market. This thesis focuses on Remaining Useful Life (RUL) estimation, which is a part of the prognosis application. By accurate RUL estimation, machines or components can be repaired or replaced before they malfunction to cause the production line or the system to stop unexpectedly. This can reduce the damage caused by an unexpected shutdown, and reduce the cost of management.
In this paper, we propose a deep learning method to construct deep neural networks for the RUL estimation. The proposed method is based on the Long Short-Term Memory (LSTM) model, which belongs to the category of Recurrent Neural Networks (RNNs). LSTM is more suitable for dealing with long-sequenced data of time series than general RNNs, and it can effectively extract and memorize significant relationship of data items which are apart from one another in the time series. It is believed that the memory characteristic in LSTM is useful for predicting RUL.
The NASA C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) data set of hundreds of propulsion engines is applied to the proposed method for performance evaluation. The evaluation results are compared with those of the MLP, SVR, RVR and CNN methods proposed in the literature. The comparisons indicate that the proposed method is the best among all compared methods in terms of the Root Mean Squared Error (RMSE) and the Scoring Function. At the end of this thesis, we describe some observations and possible application scenarios of the proposed method.

關鍵字(中)

★ 智慧工廠
★ 剩餘可用壽命
★ 深度學習
★ 深度神經網路
★ 長短期記憶

關鍵字(英)

★ Smart Factory
★ Remaining Useful Life
★ Deep Learning
★ Deep Neural Network
★ Long Short-Term Memory

論文目次

中文摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 VIII
一、緒論 1
1-1研究背景與動機 1
1-2研究目的與貢獻 2
1-3論文架構 2
二、背景知識 3
2-1人工神經網路(Artificial Neural Network, ANN) 3
2-1-1神經網路的原理 3
2-1-2神經網路的架構 4
2-1-3神經網路的學習方式 6
2-1-4倒傳遞學習演算法(Back-Propagation Algorithm) 8
2-2深度學習(Deep Learning) 9
2-2-1深度學習介紹 9
2-2-2遞歸神經網路(Recurrent Neural Network, RNN) 11
2-2-3長短期記憶(Long Short-Term Memory, LSTM) 12
2-3剩餘可用壽命(Remaining Useful Life, RUL) 16
三、問題定義與研究 18
3-1問題定義 18
3-2文獻研究 21
四、研究方法 24
4-1資料前處理 24
4-1-1標籤定義 24
4-1-2資料標準化 25
4-2網路架構 27
五、實驗與分析 30
5-1實驗環境 30
5-2實驗結果 31
5-3實驗觀察與分析 35
六、結論與未來展望 38
參考文獻 39

參考文獻

[1] Zhou Dong-Hua, Wei Mu-Heng, Si Xiao-Sheng, “A Survey on Anomaly Detection, Life Prediction and Maintenance Decision for Industrial Processes” , in ACTA AUTOMATICA SINICA 39(6):711-722, June 2014.
[2] Giduthuri Sateesh Babu, Peilin Zhao, and Xiao-Li Li, “Deep Convolutional Neural Network Based Regression Approach for Estimation of Remaining Useful Life”, S.B. Navathe et al. (Eds.): DASFAA 2016, Part I, LNCS 9642, pp. 214–228, 2016.
[3] David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams, “Learning representations by back-propagating errors”, Nature, 323, 533—536, 1986.
[4] Understanding LSTM Networks:
http://colah.github.io/posts/2015-08-Understanding-LSTMs/.
[5] Sepp Hochreiter, Jürgen Schmidhuber, “Long Short-Term Memory”, in Neural Computation 9(8):1735-80, December 1997.
[6] Rui Zhao, Ruqiang Yan, Zhenghua Chen, Kezhi Mao, Peng Wang, and Robert X. Gao, “Deep Learning and Its Applications to Machine Health Monitoring: A Survey”, Journal of Latex Class Files, Vol. 14, No. 8, August 2015.
[7] Wang, P., Youn, B.D., Hu, C., “A generic probabilistic framework for structural health prognostics and uncertainty management”, Mech. Syst. Sig. Process. 28,
622–637 (2012).
[8] T. Wang, J. Yu, D. Siegel, J. Lee, “A similarity-based prognostics approach for remaining useful life estimation of engineered systems”, in: Proceedings of the IEEE International Conference on Prognostics and Health Management(2008).
[9] Chang, C.C., Lin, C.J., “LIBSVM: a library for support vector machines”, ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011).
[10] Tipping, M.E., “The relevance vector machine”, in Solla, S.A., Leen, T.K., Muller, K.R. (eds.) Advances in Neural Information Processing Systems, vol. 12, pp. 652–658. MIT Press, Cambridge (2000).
[11] Mei Yuan, Yuting Wu, Li Lin, “Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network”, Aircraft Utility Systems (AUS), IEEE International Conference, Oct, 2016.
[12] Pankaj Malhotra, Vishnu TV, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, Gautam Shroff, “Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder”, 1st ACM SIGKDD Workshop on Machine Learning for Prognostics and Health Management, San Francisco, CA, USA, 2016.
[13] Rui Zhao, Ruqiang Yan, Jinjiang Wang, Kezhi Mao, “Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks”, Sensors (Basel) 17 (2), 2017.
[14] Neural Networks for Machine Learning: https://zh-tw.coursera.org/learn/neural-networks.
[15] Duchi, J., Hazan, E., & Singer, Y., “Adaptive Subgradient Methods for Online Learning and Stochastic Optimization”, Journal of Machine Learning Research, 12, 2121–2159, 2011.
[16] Keras Documentation: https://keras.io/.
[17] Evaluation of Deep Learning Toolkits: https://github.com/zer0n/deepframeworks/blob/master/README.md
[18] Shaohuai Shi, Qiang Wang, Pengfei Xu, Xiaowen Chu, “Benchmarking State-of-the-Art Deep Learning Software Tools”, in: Proceedings of the 7th IEEE International Conference on Cloud Computing and Big Data, Macau, China, 2016.

指導教授

江振瑞(Jehn-Ruey Jiang)

審核日期

2017-7-26

推文