基於LSTM方法於塗佈機異常分類之研究

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姓名

簡義宭(Yi-Chun Chien) 查詢紙本館藏

畢業系所

工業管理研究所

論文名稱

基於LSTM方法於塗佈機異常分類之研究
(Anomaly Classification for Coating Machine Based on LSTM Approach)

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摘要(中)

隨著工業 4.0 的推行，智慧化、數位化及自動化是製造產業發展的重要趨勢，大多數的工廠也漸漸的將智慧製造導入，希望能藉此提高生產效率、降低設備故障率及能源的消耗、敏捷製造和產品改善。這也代表機械必須朝「精密化」、「智慧化」的方向發展，以具備故障預測、精度補償、自動排程等功能。機器的穩定度成了多數工廠在乎的問題，如何有效且精確地執行維護策略是多數工廠必須面對的問題，因此近年來預測性
維護逐漸被推廣出來。基於前述，本研究主要希望能在設備發生故障前發現異常，以利維護人員提前進行修護，目的在於提早修復異常、避免產出不良品及機器的停機。
本研究使用 A 公司所提供塗佈機上感測器的歷史數據進行分析，以預測性維護（Predictive maintenance）為主要目標。分別使用主成分分析（Principal Component Analysis, PCA）以及羅吉斯迴歸（Logistic regression）進行降維，再利用長短期記憶神經網路（Long Short-Term Memory, LSTM）來建置模型，以利維修人員能提前做出決策，確保生產線的順暢，進而降低損失。

摘要(英)

With the implementation of Industry 4.0, intelligence, digitalization and automation are important trends in the development of the manufacturing industry. Most factories have gradually introduced smart manufacturing, hoping to improve production efficiency, reduce equipment failure rates and energy consumption, Agile manufacturing and product improvement. This also means that machinery and equipment must develop in the direction of "precision" and "intelligence" to have functions such as failure prediction, accuracy compensation, and automatic scheduling. The stability of the machine has become a problem that most factories care about. How to effectively and accurately implement maintenance strategies is a problem that most factories must face. Therefore, predictive maintenance has
gradually been promoted in recent years. Based on the foregoing, this research mainly hopes to find abnormalities before equipment failures, so that maintenance personnel can repair them in advance. The purpose is to repair abnormalities early and avoid production of defective
products and machine shutdowns.
This study uses the historical data of the sensors on the coating machine provided by Company A for analysis, with predictive maintenance as the main goal. Principal Component
Analysis (PCA) and Logistic regression are used fordimensionality reduction, and Long ShortTerm Memory (LSTM) is used to build the model to facilitate maintenance. So that personnel can make decisions in advance to ensure the smoothness of the production line, thereby reducing losses.

關鍵字(中)

★ 智慧製造
★ 預測性維護
★ 主成分分析
★ 羅吉斯迴歸
★ 長短期記憶網路

關鍵字(英)

論文目次

中文摘要.....................................................................................................................................i
Abstract.......................................................................................................................................ii
目錄.......................................................................................................................................... iii
圖目錄........................................................................................................................................v
表目錄.......................................................................................................................................ix
一、緒論....................................................................................................................................1
1-1 研究背景與動機........................................................................................................1
1-2 研究目的....................................................................................................................2
1-3 研究流程與架構........................................................................................................3
二、文獻探討............................................................................................................................4
2-1 深度學習....................................................................................................................4
2-2 長短期神經網路........................................................................................................7
2-3 主成分分析..............................................................................................................10
2-4 羅吉斯迴歸..............................................................................................................11
三、研究方法..........................................................................................................................13
3-1 研究對象..................................................................................................................13iv
3-2 問題定義..................................................................................................................14
3-3 主成分分析..............................................................................................................14
3-4 羅吉斯迴歸..............................................................................................................17
3-5 長短期記憶網路......................................................................................................21
3-6 評價指標（Evaluation）........................................................................................29
四、實驗與分析......................................................................................................................31
4-1 實驗環境與使用工具..............................................................................................31
4-2 實驗設計..................................................................................................................32
4-3 實驗結果..................................................................................................................46
五、結論與未來研究方向......................................................................................................49
5-1 結論..........................................................................................................................49
5-2 未來研究建議..........................................................................................................50
參考文獻..................................................................................................................................51

參考文獻

1. 莉森揪（2018）, 深度學習 vs. 傳統機器學習.
2. 行銷產業科學（2019）, 主成分分析的概念及應用
3. CodingNote.cc長短期記憶神經網路（LSTM）介紹以及簡單應用分析（2019）
4. Alex Sherstinsky.（2020）. Fundamentals of Recurrent Neural Network（RNN） and Long Short-Term Memory（LSTM）network. Physica D: Nonlinear Phenomena, 132306.
5. Ayodele, T. O.（2010）. Types of Machine Learning Algorithms.
6. Brownlee, J.（2018）. How to Develop LSTM Models for Time Series Forecasting. Available from < https://machinelearningmastery.com/how-to-develop-lstm-models-for-time-series-forecasting/ >
7. Brownlee, J. What is Deep Learning ?（2020）.
Available from < https://machinelearningmastery.com/what-is-deep-learning/ >
8. Cheng, J. C. P., Chen, W., Chen, K., Wang, Q.（2020）. Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms. Automation in Construction Volume 112（2020）, 103087
9. Choi, S. W., Martin, E. B., Morris, A. J.（2005）. Fault Detection Based on a Maximum-Likelihood Principal Component Analysis（PCA）Mixture. Ind. Eng. Chem. Res. 2005, 44, 2316-2327
10. Chung, J., Gulcehre, C., Cho, K., Bengio, Y.（2014）. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv：1412.3555
11. Davis, J., Goadrich, M.（2006）. The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd international conference on Machine learning, pp. 233–240.
12. Efthymioua, K., Papakostasa, N., Mourtzisa, D., Chryssolouris, G.（2012）. On a Predictive Maintenance Platform for Production Systems. Procedia CIRP Volume 3, 2012, Pages 221-226.
13. Grall, A., Dieulle, L., Bérenguer, C., Roussignol, M.（2002）. Continuous-Time Predictive-Maintenance Scheduling for a Deteriorating System. IEEE Transactions on reliability, VOL. 51, NO. 2, JUNE 2002
14. Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R., Schmidhuber, J.（2017）. LSTM: A Search Space Odyssey. IEEE Trans. Neural Netw. Learn. Syst., vol. 28, no. 10, pp. 2222–2232, Oct. 2017.
15. Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., Lew, M. S.（2016）. Deep learning for visual understanding: A review. Neurocomputing, 187, 27–48.
16. Hao, Q., Xue, Y., Shen, W., Jones, B., Zhu, J.（2010）. A decision support system for integrating corrective maintenance, preventive maintenance, and condition-based maintenance. Innovation for Reshaping Construction Practice (2010).
17. Hashemian, H. M.（2010）. State-of-the-Art Predictive Maintenance Techniques. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 60, NO. 1, JANUARY 2011
18. Heng, A., Tan, A. C. C., Mathew, J., Montgomery, N., Banjevic, D., Jardine, A. K. S.（2009）. Intelligent condition-based prediction of machinery reliability. Volume 23, Issue 5, July 2009, Pages 1600-1614
19. Horenbeek , A. V., Pintelon, L.（2013）. A dynamic predictive maintenance policy for complex multi-component systems. Reliability Engineering & System Safety Volume 120, December 2013, Pages 39-50
20. Kaiser, K. A., Gebraeel, N. Z.（2009）. Predictive Maintenance Management Using Sensor-Based Degradation Models. IEEE Transactions on systems, man, and cybernetics—part a: systems and humans, vol. 39, no. 4, july 2009
21. Kalchbrenner, N., Danihelka, I., Graves, A.（2015）. GRID LONG SHORT-TERM MEMORY. Under review as a conference paper at ICLR 2016
22. Kim, T. Y., Cho, S. B.（2019）. Predicting residential energy consumption using CNN-LSTM neural networks. Energy 182, 2019, Pages 72-81
23. Langone, R., Alzate, C., Ketelaere, B. D., Suykens, J. A. K.（2013）. Kernel spectral clustering for predicting maintenance of industrial machines. 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)
24. Li, H., Parikh, D., He, Q., Qian, B., Li, Z., Fang, D., Hampapur, A.（2014）. Improving rail network velocity: A machine learning approach to predictive maintenance. Transportation Research Part C: Emerging Technologies Volume 45, August 2014, Pages 17-26
25. Li, S., Li, W., Cook, C., Zhu, C., Gao, Y.（2018）. Independently Recurrent Neural Network（IndRNN）: Building A Longer and Deeper RNN. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition（CVPR）, 2018, pp. 5457-5466
26. Maind, S. B., Wankar, P.（2010）. Research Paper on Basic of Artificial Neural Network. International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169
27. Malhotra, P., Vig, L., Shroff, G., Agarwal, P.（2015）. Long Short Term Memory Networks for Anomaly Detection in Time Series. ESANN 2015 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
28. Olah, C. Understanding LSTM networks.（2015）.
Available from < http://colah.github.io/posts/2015-08-Understanding-LSTMs/ >
29. Panchal, G., Ganatra, A., Kosta, Y. P., Panchal, D.（2011）. Behaviour Analysis of Multilayer Perceptrons with Multiple Hidden Neurons and Hidden Layers. International Journal of Computer Theory and Engineering, ISSN：1793-8201
30. Paolanti, M., Romeo, L., Felicetti, A., Mancini, A., Frontoni, E., Loncarski, J.（2018）. Machine Learning approach for Predictive Maintenance in Industry 4.0. 2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)
31. Sandro Sperandei.（2014）. Understanding logistic regression analysis. Biochemia medica, 24（1）, 12–18.
32. Shrestha, A., Mahmood, A.（2019）. Review of Deep Learning Algorithms and Architectures. IEEE Access, 7, 53040–53065.
33. Song, F., Guo, Z., Mei, D.（2010）. Feature Selection Using Principal Component Analysis. 2010 International Conference on System Science, Engineering Design and Manufacturing Informatization.
34. Susto, G. A., Schirru, A., Pampuri, S., McLoone, S.（2015）. Machine Learning for Predictive Maintenance: A Multiple Classifier Approach. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 11, NO. 3, JUNE 2015
35. Sutskever, I., Martens, J., Dahl, G., Hinton, G.（2013）. On the importance of initialization and momentum in deep learning. Proceedings of the 30th International Conference on Machine Learning (ICML-13), Vol. 28, pp. 1139–1147.
36. Swanson, D. C.（2001）. A General Prognostic Tracking Algorithm for Predictive Maintenance. Aerospace Conference, 2001, IEEE Proceedings. Volume: 6
37. Tieleman, T.,　Hinton, G. E.（2012）. Neural networks for machine learning. Coursera　Lecture 65-RMSprop.
38. Wang, K.（2016）. Intelligent Predictive Maintenance (IPdM) system – Industry 4.0 scenario. WIT Transactions on Engineering Sciences, Vol 113
39. Welz, Z. A.（2017）. Integrating Disparate Nuclear Data Sources for Improved Predictive Maintenance Modeling：Maintenance-Based Prognostics for Long-Term Equipment Operation.
40. Xu, P., Du, R., Zhang, Z.（2019）. Predicting pipeline leakage in petrochemical system through GAN and LSTM. Knowledge-Based Systems 175 （2019） 50-61

指導教授

陳振明

審核日期

2021-7-6

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