基於LSTM方法於建置預診斷與健康管理模型—以塗佈機為例

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陳嘉瑩(Jia-Ying Chen) 查詢紙本館藏

畢業系所

工業管理研究所

論文名稱

基於LSTM方法於建置預診斷與健康管理模型—以塗佈機為例
(Developing a Prognostics and Health Management Model Based on LSTM Approach – A Case Study of Coating Machine)

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摘要(中)

在工業4.0 的浪潮下，全球製造業以「智慧製造」為主軸，朝向「智動化」的目標發展，精準地制定維護策略有助於提高生產效率及產品品質，成為智慧製造的關鍵議題之一。物聯網（Internet of Things, IoT）、感測器（Sensor）、數據分析（Big Data）、雲端運算（Cloud Computing）及人工智慧（Artificial Intelligence, AI）等日臻成熟的先進技術，促使預測性維護得以實現。許多機器設備都配置了感測器並結合物聯網收集大量的數據，然而，更重要的任務是從數據資料中挖掘出可利用的資訊，並做出更好的決策，才能使資料被物盡其用，發揮最大的價值。
本研究欲使用A公司所提供之塗佈機，以預診斷與健康管理（Prognostics and Health Management, PHM）為主題改善該公司原有的預防性維護策略，建立塗佈機的預測模型。透過深度學習演算法中的長短期記憶（Long Short-Term Memory, LSTM）神經網路結合自動編碼器（Autoencoder）作為建置模型的方法，從塗佈機運作的歷史數據中分析後獲得規律，找出判斷異常狀況的徵兆以對機台進行異常檢測與條件監控，並建立評估機台健康狀況之指標。預測結果與實際狀況相比，可以於異常前10秒檢測到徵兆，並與實際張力異常紀錄相比提早20秒判斷出異常，加總之下爭取到30秒的反應時間，有助於人員能在停機前做出適切的維護決策，確保生產線的正常運作，進而達到預測維護之目的。

摘要(英)

Under the trend of Industry 4.0, manufacturing aims at achieving “Intelligent Manufacturing”. Formulating the maintenance strategy accurately contributes to improving the production efficiency and product quality. Recently, significant attention has been paid to predictive maintenance. The rapid development of science and technology, such as the IoT, sensor, cloud computing, and AI has paved the way for the predictive maintenance. Most of the machines have assembled sensors connecting to IoT to acquire data. To make the best of the data, companies should focus on how to convert the data into valuable information to support maintenance decision making.
This study uses the production dataset of the coating machine provided by Company A, intending to conduct a health assessment while establishing a prediction model based on the Prognostics and Health Management framework, thereby improving the company′s original preventive maintenance strategy. Combing Autoencoder with LSTM neural network to construct a predictive model, extracting the characteristics through the historical data transferred from the coating machine for condition monitoring and finding out the signs of an anomaly when there is an abnormal situation. Then, build up the health index to do a health assessment for the coating machine.
The results show that the predicted model can detect 30 seconds in total earlier than the actual abnormal record, thereby enabling personnel to make appropriate maintenance decisions within the reaction time before the breakdown, maximizing the uptime of equipment while avoiding disruption to production and reaching the predictive maintenance.

關鍵字(中)

★ 預診斷與健康管理
★ 異常檢測
★ 深度學習
★ 長短期記憶網路
★ 自動編碼器

關鍵字(英)

★ Prognostics and Health Management
★ Anomaly Detection
★ Deep Learning
★ Long Short-Term Memory Network
★ Autoencoder

論文目次

中文摘要 I
ABSTRACT II
目錄 III
圖目錄 V
表目錄 VIII
一、緒論 1
1-1 研究背景與動機 1
1-2 研究目的 2
1-3 研究假設與限制 3
1-4 研究流程與架構 3
二、文獻探討 6
2-1 維護策略 6
2-2 預診斷與健康管理（PROGNOSTICS AND HEALTH MANAGEMENT, PHM） 8
2-2-1 異常檢測（Anomaly Detection） 9
2-2-2 剩餘可用壽命（Remaining Useful Life, RUL） 10
2-3 預診斷與健康管理的架構 11
2-4 預診斷與健康管理的預測方法 13
2-4-1 機器學習（Machine Learning, ML） 15
2-4-2 深度學習（Deep Learning, DL） 16
2-4-3 時間序列分析（Time Series Analysis） 18
2-5 文獻小結 19
三、研究方法 21
3-1 研究對象 21
3-2 問題定義 23
3-3 資料前處理（DATA PREPROCESSING） 26
3-3-1 特徵標準化（Feature Scaling） 28
3-4 長短期記憶（LONG SHORT-TERM MEMORY） 29
3-4-1 長短期記憶自動編碼器（LSTM Autoencoder） 31
3-4-2 激勵函數（Activation Function） 32
3-4-3 損失函數（Loss Function） 34
3-4-4 優化器（Optimizer） 34
3-5 評價指標（EVALUATION METRICS） 36
四、實驗結果與分析 38
4-1 實驗環境與開發工具 38
4-2 實驗設計 39
4-2-1 資料集說明 41
4-2-2 模型建立 43
4-3 實驗結果 44
4-4 實驗分析與討論 49
4-4-1 異常檢測 49
4-4-2 健康指數 51
五、結論與未來展望 54
5-1 結論 54
5-2 未來研究建議 55
參考文獻 56

參考文獻

1. Adewumi, A. O., & Akinyelu, A. A. (2017). A survey of machine-learning and nature- inspired based credit card fraud detection techniques. International Journal of System Assurance Engineering and Management, 8(2), 937-953.
2. Atamuradov, V., Medjaher, K., Dersin, P., Lamoureux, B., & Zerhouni, N. (2017). Prognostics and health management for maintenance practitioners-review, implementation and tools evaluation. International Journal of Prognostics and Health Management, 8(060), 1-31.
3. Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks, 5(2), 157-166.
4. Borghesi, A., Bartolini, A., Lombardi, M., Milano, M., & Benini, L. (2019, July). Anomaly detection using autoencoders in high performance computing systems. In Proceedings of the AAAI Conference on Artificial Intelligence 33, 9428-9433.
5. Cachada, A., Barbosa, J., Leitño, P., Gcraldcs, C. A. S., Deusdado, L., Costa, J., Teixeira, C., Teixeira, J., Moreira, A. H. J., Moreira, P. M., & Romero, L. (2018). Maintenance 4.0: Intelligent and Predictive Maintenance System Architecture. In 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA), 139-146.
6. Chen, X., Yu, J., Tang, D., & Wang, Y. (2011, August). Remaining useful life prognostic estimation for aircraft subsystems or components: A review. In IEEE 2011 10th International Conference on Electronic Measurement & Instruments 2, 94-98.
7. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
8. Coleman, C., Damodaran, S., Chandramouli, M., & Deuel, E. (2017). Making maintenance smarter: Predictive maintenance and the digital supply network. Deloitte Insights.
9. Cook, A., Mısırlı, G., & Fan, Z. (2019). Anomaly detection for IoT time-series data: A survey. IEEE Internet of Things Journal.
10. Das, S., Hall, R., Herzog, S., Harrison, G., Bodkin, M., & Martin, L. (2011). Essential steps in prognostic health management. In 2011 IEEE Conference on Prognostics and Health Management, 1-9.
11. Duchi, J., Hazan, E., & Singer, Y. (2011). Adaptive subgradient methods for online learning and stochastic optimization. Journal of machine learning research, 12(7).
12. Guillén, A. J., Crespo, A., Macchi, M., & Gómez, J. (2016). On the role of Prognostics and Health Management in advanced maintenance systems. Production Planning & Control, 27(12), 991-1004.
13. Hawkins, D. M. (1980). Identification of outliers 11. London: Chapman and Hall.
14. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
15. Johnson, A. C., Aleshkova, N. D., Barker, P. F., Golynsky, A. V., Masolov, V. N., & Smith, A. M. (1992). A preliminary aeromagnetic anomaly compilation map for the Weddell province of Antarctica.
16. Kang, M., & Jameson, N. J. (2018). Machine Learning: Fundamentals. Prognostics and Health Management of Electronics: Fundamentals, Machine Learning, and the Internet of Things, 85-109.
17. Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15-25.
18. Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
19. Krar, S. (2015). The IMPORTANCE of MAINTENANCE (Changing from a FAIL and FIX Approach to a PREDICT and PREVENT Approach). Retrieved May, 2, 2016.
20. Kwon, D., Kim, H., Kim, J., Suh, S. C., Kim, I., & Kim, K. J. (2019). A survey of deep learning-based network anomaly detection. Cluster Computing, 1-13.
21. Lebold, M., Reichard, K., Byington, C. S., & Orsagh, R. (2002, May). OSA-CBM architecture development with emphasis on XML implementations. In Maintenance and Reliability Conference (MARCON) 6-8.
22. Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications. Mechanical systems and signal processing, 42(1-2), 314-334.
23. Lee, J., Jin, C., Liu, Z., & Ardakani, H. D. (2017). Introduction to data-driven methodologies for prognostics and health management. In Probabilistic prognostics and health management of energy systems 9-32.
24. Li, Z., Wang, Y. & Wang, K. Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario. 2017 Advances in Manufacturing, 5(4), 377–387.
25. Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical image analysis, 42, 60-88.
26. Malhotra, P., TV, V., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P., & Shroff, G. (2016). Multi-sensor prognostics using an unsupervised health index based on LSTM encoder-decoder. arXiv preprint arXiv:1608.06154.
27. Monitoring, C. (2004). Diagnostics of machines-prognostics part 1: General guidelines. ISO13381-1:(e). vol. ISO/IEC Directives Part 2, IO f. S, 14.
28. Mosallam, A., Medjaher, K., & Zerhouni, N. (2016). Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction. Journal of Intelligent Manufacturing, 27(5), 1037-1048.
29. Nuttall, N. (2016). Trenitalia drives cost savings using IoT on train operations.
Gartner, December 2016.
30. Okoh, C., Roy, R., Mehnen, J., & Redding, L. E. (2014). Overview of remaining useful life prediction techniques in through-life engineering services.
31. Ramotsoela, D., Abu-Mahfouz, A., & Hancke, G. (2018). A survey of anomaly detection in industrial wireless sensor networks with critical water system infrastructure as a case study. Sensors, 18(8), 2491.
32. Remadna, I., Terrissa, S. L., Zemouri, R., & Ayad, S. (2018). An overview on the Deep Learning based prognostic. 2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET), 196–200.
33. Serradilla, O., Zugasti, E., & Zurutuza, U. (2020). Deep learning models for predictive maintenance: a survey, comparison, challenges and prospect. arXiv preprint arXiv:2010.03207.
34. Shrestha, A., & Mahmood, A. (2019). Review of deep learning algorithms and architectures. IEEE Access, 7, 53040-53065.
35. Sikorska, J. Z., Hodkiewicz, M., & Ma, L. (2011). Prognostic modelling options for remaining useful life estimation by industry. Mechanical systems and signal processing, 25(5), 1803-1836.
36. Srivastava, N., Mansimov, E., & Salakhudinov, R. (2015, June). Unsupervised learning of video representations using lstms. In International conference on machine learning 843-852.
37. Sublime, J., & Kalinicheva, E. (2019). Automatic post-disaster damage mapping using deep-learning techniques for change detection: Case study of the Tohoku tsunami. Remote Sensing, 11(9), 1123.
38. Sun, B., Zeng, S., Kang, R., & Pecht, M. (2010). Benefits analysis of prognostics in systems. In 2010 Prognostics and System Health Management Conference, 1-8.
39. Sun, F., Wang, N., Li, X., & Zhang, W. (2017). Remaining useful life prediction for a machine with multiple dependent features based on Bayesian dynamic linear model and copulas. Ieee Access, 5, 16277-16287.
40. Sutharssan, T., Stoyanov, S., Bailey, C., & Yin, C. (2015). Prognostic and health management for engineering systems: a review of the data-driven approach and algorithms. The Journal of engineering, 2015(7), 215-222.
41. Tharwat, A. (2020). Classification assessment methods. Applied Computing and Informatics.
42. Tieleman, T., & Hinton, G. E. (2012). Neural networks for machine learning. Coursera (Lecture 65-RMSprop).
43. Tinga, T., & Loendersloot, R. (2014). Aligning PHM, SHM and CBM by understanding the physical system failure behaviour. In European Conference of the Prognostics and Health Management Society.
44. Tsui, K. L., Chen, N., Zhou, Q., Hai, Y., & Wang, W. (2015). Prognostics and health management: A review on data driven approaches. Mathematical Problems in Engineering.
45. Vachtsevanos, G. J., Lewis, F., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent fault diagnosis and prognosis for engineering systems 456. Hoboken: Wiley.
46. Vogl, G. W., Weiss, B. A., & Helu, M. (2019). A review of diagnostic and prognostic capabilities and best practices for manufacturing. Journal of Intelligent Manufacturing, 30(1), 79-95.
47. Wan, J., Tang, S., Li, D., Wang, S., Liu, C., Abbas, H., & Vasilakos, A. V. (2017). A manufacturing big data solution for active preventive maintenance. IEEE Transactions on Industrial Informatics, 13(4), 2039-2047.
48. Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, 48, 144- 156.
49. Welz, Z. A. (2017). Integrating Disparate Nuclear Data Sources for Improved Predictive Maintenance Modeling: Maintenance-Based Prognostics for Long-Term Equipment Operation.
50. Wise, R., & Baumgartner, P. (1999). Go Downstream: The New Profit Imperative in Manufacturing. Harvard Business Review (September-October), 133-141.
51. Xia, T., Dong, Y., Xiao, L., Du, S., Pan, E., & Xi, L. (2018). Recent advances in prognostics and health management for advanced manufacturing paradigms. Reliability Engineering & System Safety, 178, 255-268.
52. Xing, W., & Du, D. (2019). Dropout prediction in MOOCs: Using deep learning for personalized intervention. Journal of Educational Computing Research, 57(3), 547- 570.
53. Zhao, G., Zhang, G., Ge, Q., & Liu, X. (2016, October). Research advances in fault diagnosis and prognostic based on deep learning. In 2016 Prognostics and system health management conference (PHM-Chengdu) 1-6.
54. Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115, 213-237.

指導教授

陳振明(Jen-Ming Chen)

審核日期

2021-1-14

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