基於Autoformer與時序卷積網路建構預測剩餘失效時間的混合模型

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姓名

曾元慶(Yuan-Ching Tseng) 查詢紙本館藏

畢業系所

工業管理研究所

論文名稱

基於Autoformer與時序卷積網路建構預測剩餘失效時間的混合模型
(A Hybrid Model Based on Autoformer and Temporal Convolutional Network for Remaining Useful Life Prediction)

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摘要(中)

在當今工業和科技不斷進步的背景下，設備健康監測(PHM)和預測剩餘失效時間（RUL）成為了工業管理中至關重要的部分。準確地預測設備的剩餘失效時間有助於提高設備的可靠性、降低維護成本，並優化生產計劃。然而，傳統的 RUL 預測方法在面對複雜多變的時間序列數據時面臨一些挑戰。這包括對設備狀態變化的準確捕捉以及長期依賴關係的建模。在大數據和機器學習的時代，如何利用網路上多元的資料做進一步的研究是現今熱門的課題。本研究旨在解決傳統 Transformer 的注意力機制在長序列預測上難以發現可靠的時序依賴的問題，提高運算效率及記憶體使用的優化；提升對局部序列特徵的提取能力；降低異常值對時間序列的影響，建立有效的預測模型幫助企業降低風險，提高設備維護效率，減少停機時間。為達成這些目標，本研究提出了一個混合模型，結合了 Autoformer 模型、STL 時序分解方法和時序卷積網路。這些方法的結合將有助於更準確地預測設備的剩餘失效時間，提高設備管理效率和生產效率。

摘要(英)

In the context of today′s industrial and technological advances, equipment health monitoring (PHM) and predicted remaining life (RUL) have become a critical part of industrial management. Accurately predicting the remaining life of equipment can help improve equipment reliability, reduce maintenance costs, and optimize production schedules. However, traditional RUL prediction methods face a number of challenges when dealing with complex and variable time-series data. These include accurately capturing changes in equipment state and modeling long-term dependencies. In the era of big data and machine learning, how to utilize the multifarious data on the Internet for further research is a hot topic nowadays. In this study, we aim to solve the problem that the traditional attention mechanism of Transformer is difficult to find reliable time series
dependencies in long sequence prediction, to improve the computational efficiency and optimize the memory usage, to enhance the ability of extracting local sequence features,
to reduce the impact of anomalies on time series, to build an effective prediction model to help enterprises to reduce the risk, to improve the efficiency of equipment maintenance,and to reduce the downtime. To achieve these goals, this study proposes a hybrid model that combines the Autoformer model, the STL time-series decomposition method, and the time-series convolutional network. The combination of these methods will help to predict the remaining life of equipment more accurately and improve the efficiency of
equipment management and productivity.

關鍵字(中)

★ 預測與健康維護
★ 預測剩餘失效時間
★ 時序卷積網路
★ Autoformer

關鍵字(英)

★ Prognostics Health Management
★ Remaining Useful Life
★ Temporal Convolutional Network
★ Autoformer
★ ime series forecasting

論文目次

摘要 ........................................................... ii
Abstract ....................................................... iii
目錄 ........................................................... iv
圖目錄 ......................................................... vi
表目錄 ........................................................ vii
第一章緒論..................................................... 1
1.1 研究背景與動機........................................... 1
1.2 研究問題................................................. 2
1.3 研究目的................................................. 3
1.4 研究方法................................................. 3
1.5 研究架構................................................. 4
第二章文獻回顧 ................................................. 5
2.1 剩餘失效時間預測相關研究................................. 5
2.2 Autoformer ............................................... 6
2.2.1 深度分解架構(Deep Decomposition Framework) .......... 7
2.2.2 自相關機制(Auto-Correlation Mechanism) ............... 8
2.3 STL 序列分解單元......................................... 9
2.4 時序卷積網路(Temporal Convolutional Network, TCN).......... 10
第三章研究方法 ................................................ 13
3.1 模型設計 ................................................ 13
3.2 自相關機制(Auto-Correlation Mechanism) .................... 14
3.2.1 高效計算(Efficient computation)....................... 16
3.3 STL 序列分解單元 ........................................ 16
3.4 時序卷積網路(Temporal Convolutional Network, TCN).......... 19
3.4 .1 因果卷積(Causal Convolution) ....................... 19
3.4 .2 空洞卷積/膨脹卷積(Dilated Convolution) .............. 19
3.4 .3 殘差模塊(Residual block) ........................... 20
3.5 編碼器、解碼器.......................................... 19
第四章實驗 .................................................... 23
4.1 原始資料 ................................................ 23
4.2 監測數據篩選與資料前處理 ................................ 25
4.2.1 監測數據篩選 ...................................... 25
4.2.2 資料前處理 ........................................ 27
4.3 評估指標................................................ 29
4-4 實驗設置................................................ 30
4-5 實驗結果................................................ 32
第五章結論與未來方向 .......................................... 36
參考文獻 ....................................................... 37

參考文獻

[1] Akaike, H. "Maximum likelihood identification of Gaussian autoregressive
moving average models." Biometrika 60.2, 1973, 255-265.
[2] Asadi, R., A. C. Regan. "A spatio-temporal decomposition based deep neural
network for time series forecasting." Applied Soft Computing 87, 2020, 105963.
[3] Bai, J.Z. Kolter, V. Koltun. "An empirical evaluation of generic convolutional and
recurrent networks for sequence modeling." arXiv preprint
arXiv:1803.01271, 2018.
[4] Cleveland, R. B., W. S. Cleveland, J. E. McRae, I. Terpenning. "STL: A seasonaltrend decomposition." J. off. Stat 6.1, 1990, 3-73.
[5] Fan, J., K. C. Yung, M. Pecht. "Physics-of-failure-based prognostics and health
management for high-power white light-emitting diode lighting." IEEE
Transactions on Device and Materials Reliability 11.3, 2011, 407-416.
[6] Galar, D., U. Kumar, Y. Fuqing. "RUL prediction using moving trajectories
between SVM hyper planes." 2012 Proceedings Annual Reliability and
Maintainability Symposium., 2012, 1-6.
[7] Guo, L., N. Li, F. Jia, Y. Lei, J. Lin. "A recurrent neural network based health
indicator for remaining useful life prediction of bearings." Neurocomputing 240,
2017, 98-109.
[8] He, K., X. Zhang, S. Ren, J. Sun. "Deep residual learning for image
recognition." Proceedings of The IEEE Conference on Computer Vision and
Pattern Recognition., 2016, 770-778.
[9] Hyndman, R. J., G. Athanasopoulos. Forecasting: Principles and Practice.
OTexts, 2018.
[10] Kacprzynski, G. J., A. Sarlashkar, M. J. Roemer, A. Hess, B. Hardman "Predicting
remaining life by fusing the physics of failure modeling with diagnostics.
" JOm 56, 2004, 29-35.
[11] Keshun, Y., Q. Guangqi, G. Yingkui. "A 3D attention-enhanced hybrid neural
network for turbofan engine remaining life prediction using CNN and Bi-LSTM
models." IEEE Sensors Journal, 2023.
[12] Kitaev, N., Ł. Kaiser, A. Levskaya. "Reformer: The efficient transformer." arXiv
preprint arXiv:2001.04451, 2020.
[13] Li, H., W. Zhao, Y. Zhang, E. Zio. "Remaining useful life prediction using multiscale deep convolutional neural network." Applied Soft Computing 89, 2020, 106-
113.
[14] Li, S., X. Jin, Y. Xuan, X. Zhou, W. Chen, Y. X. Wang, X. Yan. "Enhancing the
locality and breaking the memory bottleneck of transformer on time series
forecasting." Advances in Neural Information Processing Systems 32, 2019.
[15] Li, S., X. Jin, Y. Xuan, X. Zhou, W. Chen, Y. X. Wang, X. Yan. "Enhancing the
locality and breaking the memory bottleneck of transformer on time series
forecasting." Advances in Neural Information Processing Systems 32, 2019.
[16] Li, X., Q. Ding, J. Q. Sun. "Remaining useful life estimation in prognostics using
deep convolution neural networks." Reliability Engineering & System Safety 172,
2018, 1-11.
[17] Li, X., Y. Xu, N. Li, B. Yang, Y. Lei. "Remaining useful life prediction with partial
sensor malfunctions using deep adversarial networks." IEEE/CAA Journal of
Automatica Sinica 10.1, 2022, 121-134.
[18] Li, Y., S. Billington, C. Zhang, T. Kurfess, S. Danyluk, S. Liang. "Adaptive
prognostics for rolling element bearing condition." Mechanical Systems and
Signal Processing 13.1, 1999, 103-113.
[19] Lin, Y., I. Koprinska, M. Rana. "Temporal convolutional attention neural networks
for time series forecasting." 2021 International Joint Conference on Neural
Networks (IJCNN), 2021,1-8
[20] Liu, L., X. Song, Z. Zhou. "Aircraft engine remaining useful life estimation via a
double attention-based data-driven architecture." Reliability Engineering &
System Safety 221, 2022, 108330.
[21] Miao, J., X. Li, J. Ye. "Predicting research of mechanical gyroscope life based on
wavelet support vector." 2015 First International Conference on Reliability
Systems Engineering (ICRSE)., 2015.
[22] Mo, Y., Q. Wu, X. Li, B. Huang. "Remaining useful life estimation via
transformer encoder enhanced by a gated convolutional unit." Journal of
Intelligent Manufacturing 32.7, 2021, 1997-2006.
[23] Nieto, P. J. G., E. García-Gonzalo, F. S. Lasheras, F. J. de Cos Juez. "Hybrid
PSO–SVM-based method for forecasting of the remaining useful life for aircraft
engines and evaluation of its reliability." Reliability Engineering & System
Safety 138, 2015, 219-231.
[24] Oppenheimer, C.H., K. A. Loparo. "Physically based diagnosis and prognosis of
cracked rotor shafts." Component and Systems Diagnostics, Prognostics, and
Health Management II. Vol. 4733. SPIE, 2002, 122-132.
[25] Oreshkin, B. N., D. Carpov, N. Chapados, Y. Bengio. "N-BEATS: Neural basis
expansion analysis for interpretable time series forecasting." arXiv preprint
arXiv:1905.10437, 2019.
[26] Papoulis, A. Random Variables and Stochastic Processes. McGraw Hill, 1965.
[27] Parzen, E. "An approach to time series analysis." The Annals of Mathematical
Statistics 32.4, 1961, 951-989.
[28] Peddinti, V., D. Povey, S. Khudanpur. "A time delay neural network architecture
for efficient modeling of long temporal contexts." Interspeech, 2015, 3214-3218.
[29] Qin, Y., D. Song, H. Chen, W. Cheng, G. Jiang, G. Cottrell. "A dual-stage
attention-based recurrent neural network for time series prediction." arXiv
preprint arXiv:1704.02971, 2017.
[30] Ramasso, E., A. Saxena. "Performance Benchmarking and Analysis of Prognostic
Methods for CMAPSS Datasets." International Journal of Prognostics and Health
Management 5.2, 2014, 1-15.
[31] Ruan, D., Y. Wu, J. Yan, C. Gühmann. "Fuzzy-membership-based framework for
task transfer learning between fault diagnosis and RUL prediction." IEEE
Transactions on Reliability 72.3, 2022, 989-1002.
[32] Sateesh Babu, G., P. Zhao, X. L. Li. "Deep convolutional neural network based
regression approach for estimation of remaining useful life." Database Systems
for Advanced Applications: 21st International Conference, DASFAA 2016, Dallas,
TX, USA, April 16-19, 2016, Proceedings, Part I 21. Springer International
Publishing, 2016, 214-228.
[33] Saxena, A., K. Goebel, D. Simon, N. Eklund. "Damage propagation modeling for
aircraft engine run-to-failure simulation." 2008 international Conference on
Prognostics and Health Management., 2008, 1-9.
[34] Sen, R., H. F. Yu, I. S. Dhillon. "Think globally, act locally: A deep neural
network approach to high-dimensional time series forecasting." Advances in
Neural Information Processing Systems 32, 2019.
[35] Taylor, S. J., B. Letham. "Forecasting at scale." The American Statistician 72.1,
2018, 37-45.
[36] Tian, Z. "An artificial neural network method for remaining useful life prediction
of equipment subject to condition monitoring." Journal of Intelligent
Manufacturing 23, 2012, 227-237.
[37] Wang, D., K. L. Tsui, Q. Miao. "Prognostics and health management: A review of
vibration based bearing and gear health indicators." IEEE Access 6, 2017, 665-
676.
[38] Wu, H., J. Xu, J. Wang, M. Long. "Autoformer: Decomposition transformers with
auto-correlation for long-term series forecasting." Advances in Neural Information
Processing Systems 34, 2021, 22419-22430.
[39] Xu, X., Q. Wu, X. Li, B. Huang. "Dilated convolution neural network for remaining useful life prediction." Journal of Computing and Information Science
in Engineering 20.2, 2020, 021004.
[40] Yu, W., I. I. Y. Kim, C. Mechefske. "Remaining useful life estimation using a
bidirectional recurrent neural network based autoencoder scheme." Mechanical
Systems and Signal Processing 129, 2019, 764-780.
[41] Zhang, W., G. Peng, C. Li, Y. Chen, Z. Zhang. "A new deep learning model for
fault diagnosis with good anti-noise and domain adaptation ability on raw
vibration signals." Sensors 17.2, 2017, 425.
[42] Zhang, Y., R. Xiong, H. He, M. G. Pecht. "Long short-term memory recurrent
neural network for remaining useful life prediction of lithium-ion batteries." IEEE
Transactions on Vehicular Technology 67.7, 2018, 5695-5705.
[43] Zhao, R., R. Yan, Z. Chen, K. Mao, P. Wang, R. X. Gao. "Deep learning and its
applications to machine health monitoring." Mechanical Systems and Signal
Processing 115, 2019, 213-237.
[44] Zhao, S., Y. Pang, J. Chen, J. Liu. "Prediction of remaining useful life of aircraft
engines based on Multi-head Attention and LSTM." 2022 IEEE 6th Information
Technology and Mechatronics Engineering Conference (ITOEC). Vol. 6., 2022,
1530-1534.
[45] Zheng, S., K. Ristovski, A. Farahat, C. Gupta. "Long short-term memory network
for remaining useful life estimation." 2017 IEEE International Conference on
Prognostics and Health Management (ICPHM)., 2017, 88-95
[46] Zhou, H., S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong, W. Zhang. "Informer:
Beyond efficient transformer for long sequence time-series
forecasting." Proceedings of The AAAI Conference on Artificial Intelligence. Vol.
35. No. 12., 2021.

指導教授

葉英傑(Ying-Chieh Yeh)

審核日期

2024-7-22

推文