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.
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
[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.
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