基於長短期記憶神經網路方法於預測性維護之研究—以塗佈機為例

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姓名

郭倍豪(Bei-Hao Kuo) 查詢紙本館藏

畢業系所

工業管理研究所

論文名稱

基於長短期記憶神經網路方法於預測性維護之研究—以塗佈機為例
(Predictive Maintenance Based on A Long Short-Term Memory Neural Network Approach - A Case Study of Coating Machine)

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摘要(中)

因應軟硬體技術的發展，全世界的工業逐步利用物聯網、Big Data、Machine Learning等技術，從原先工業 3.0 的「自動化」邁向工業 4.0 的「智動化」，在新興的技術導入後，企業在實際生產中將有助於維持設備的高稼動率以及提升製程的穩定性。
本研究以探討工業 4.0中的預診斷與健康管理（Prognostics and Health Management, PHM）研究，提出一項基於長短期記憶神經網路（Long Short-Term Memory Neural Network, LSTM）的異常偵測方法對設備進行即時的診斷，從時間序列資料進行分析，首先透過LSTM中的記憶單元儲存過去輸入的健康狀態生產行為，在連續的時間點，預測輪軸特徵的數據走向，並透過比對預測值及實際值所產生的誤差，根據其機率密度函數設定異常閾值（Threshold）。日後在生產過程中，一旦誤差連續超出所設定之閾值界線，則進而預警設備即將出現故障，在設備因故停止運作前，提早進行機器的維護作業，以降低不預期的損壞所造成的突發損失，實現預測性維護。
本研究將所提方法應用於A企業中的塗佈機實際生產時所回饋的生產資料集，透過調整參數配置之間預測誤差的變化，找出最佳化的模型配置，之後利用最佳化模型於測試資料集中關注最早的預警時間以及預警的效能指標，實證結果闡明，基於LSTM的異常偵測方法在前後者皆有穩健的表現。

摘要(英)

In response to the development of software and hardware technologies, industries around the world are gradually using advanced technologies such as the Internet of Things, Big Data, and Machine Learning to achieve not only the "Automation" but also the "Intelligence" in the Industry 4.0 era. In the actual production, new technologies will help maintain a high utilization rate of equipment and improve the stability of manufacturing,
In this research, I explore the Prognostics and Health Management (PHM) study in Industry 4.0 and propose an anomaly detection method based on a Long Short-Term Memory Neural Network (LSTM) approach on time series data for assessing the equipment in real time. First, the memory unit in LSTM would be used to store past input health-state production behavior. Through continuous time points, algorithm will predict the data trend of the pattern. Then the error will be generated by comparing the predicted value with the actual value. Finally, setting the abnormal threshold based on its probability density function. In the future, once the error continuously exceeds the threshold in the production process, the user will receive the warning which the equipment is about to be malfunction. By assessing healthy state of the equipment in real time via anomaly detection, we can arrange early maintenance or replacement of parts before the equipment shutting down accidentally to achieve predictive maintenance.
The proposed method is applied to the production dataset feedbacked from the actual production of the coating machine from company A. First, finding the optimized model configuration by observing the difference of prediction error caused by different combination of model parameters. Second, using the optimized model investigates the earliest warning time and the indicators of predicted performance in the test dataset. Finally, the experimental results show that anomaly detection based on LSTM has robust performance on both earliest warning time and predicted performance.

關鍵字(中)

★ 智慧製造
★ 人工智慧
★ 預診斷與健康管理
★ 深度學習
★ 長短期記憶神經網路
★ 時間序列

關鍵字(英)

★ Smart Manufacturing
★ Artificial Intelligence
★ Prognostics and Health Management
★ Deep Learning
★ Long Short-Term Memory Neural Network
★ Time Series

論文目次

目錄
摘要 i
ABSTRACT ii
目錄 iii
圖目錄 iv
表目錄 v
一、緒論 1
1-1 研究背景與動機 1
1-2 研究目的與貢獻 1
1-3 研究架構 2
二、文獻探討 3
2-1 虛實系統（Cyber-Physical System） 3
2-2 預診斷與健康管理（Prognostic and Health Management） 5
2-3 長短期記憶神經網路（Long Short-Term Memory Neural Network） 7
三、研究方法 9
3-1 塗佈機 9
3-1-1 異常原因 9
3-1-2 問題定義 9
3-1-3資料集 10
3-2 長短期記憶神經網路與異常檢測 11
3-2-1 長短期記憶神經網路運算架構 11
3-2-2 資料預處理 15
3-2-3 訓練最佳化 15
3-2-4 異常偵測 18
3-2-5 預測評估標準 19
四、實驗結果與分析 21
4-1 開發工具與實驗環境 21
4-2 實驗設計 22
4-2-1 資料集分割 22
4-2-2 神經網路架構設計 22
4-3 實證分析與討論 23
4-3-1 最佳化模型配置 23
4-3-2 異常偵測視覺化 25
4-3-3 實證結果討論 32
4-3-4 預測性維護程序 33
五、結論與未來研究 34
參考文獻 35

參考文獻

[1] Shin, J. H., Jun, H. B. (2015). On condition based maintenance policy. Journal of Computational Design and Engineering, Vol.2, pp.119-127.

[2] Shi, J. H., Wan, J. F., Yan, H. H., Suo, H. (2011). A survey of cyber-physical systems. In International conference on wireless communications and signal processing (WCSP), November 2011.

[3] Lee, J., Lapira, E., Bagheri, B., Kao, H. A. (2013). Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing Letters, Vol.1, pp.38-41.

[4] Lee, J., Lapira, E., Bagheri, B., Kao, H. A. (2015). A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, Vol.3, pp.18–23.

[5] Lee J., Bagheri B. (2015). Cyber-Physical Systems in Future Maintenance. In: Amadi-Echendu J., Hoohlo C., Mathew J. (Eds.). 9th WCEAM Research Papers. Lecture Notes in Mechanical Engineering. Springer, Cham.

[6] Lee, J., Davari Ardakani H., Yang, S. H., Bagheri, B. (2015). Industrial Big Data Analytics and Cyber-physical Systems for Future Maintenance & Service Innovation. In International Conference on Through-life Engineering Services, Vol.38, pp.3-7.

[7] Lee J., Jin C., Liu Z., Davari Ardakani H. (2017). Introduction to Data-Driven Methodologies for Prognostics and Health Management. In Ekwaro-Osire S., Gonçalves A., Alemayehu F. (Eds.). Probabilistic Prognostics and Health Management of Energy Systems. Springer, Cham

[8] Pecht, M., Jaai, R. (2010). A prognostics and health management roadmap for information and electronics-rich systems. Microelectronics Reliability, Vol. 50, pp.317–323.

[9] Wang, J. J., Ma, Y. L., Zhang, L. B., Gao, R. X., Wu, D. Z. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, Vol.48, pp.144–156.

[10] Schmidhuber, J. (1989). A local learning algorithm for dynamic feedforward and recurrent networks. Connection Science, Vol.1, pp. 403–412.

[11] Hochreiter, S., Schmidhuber, J. (1997). Long short-term memory. Neural Computation, Vol.9, pp. 1735–1780.

[12] Cui, Y. M., Wang, S. J., Li, J. F. (2016). LSTM Neural Reordering Feature for Statistical Machine Translation. In Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 977–982.

[13] Venugopalan, S., Hendricks, L. A., Mooney, R., Saenko, K. (2016). Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text. In Conference on Empirical Methods in Natural Language Processing, pp. 1961–1966.

[14] Alguliyev, R. M., Aliguliyev, R. M., Abdullayeva, F. J. (2019). The Improved LSTM and CNN Models for DDoS Attacks Prediction in Social Media. International Journal of Cyber Warfare and Terrorism, Vol. 9, pp.1-18.

[15] Pouladi, F., Salehinejad, H., Gilani, A. M. (2015). Recurrent Neural Networks for Sequential Phenotype Prediction in Genomics. In International Conference on Developments of E-Systems Engineering, pp.225-230.

[16] Malhotra, P., Vig, L. , Shroff, G., Agarwal, P. (2015). Long short-term memory networks for anomaly detection in time series. In: Proceeding of European symposium on artificial neural networks, computational intelligence, and machine learning. pp. 89–94.

[17] Liao, L. X., Ahn, H. I. (2016). Combining Deep Learning and Survival Analysis for Asset Health Management. International Journal of Prognostics and Health Management, Vol.16, pp.1-7.

[18] Zhao, R., Wang, J., Yan, R., Mao, K. (2016). Machine Health Monitoring with LSTM Networks. In International Conference on Sensing Technology (ICST).

[19] Park, D., Kim, S., An, Y., Jung, J. Y. (2018). LiReD: A Light-Weight Real-Time Fault Detection System for Edge Computing Using LSTM Recurrent Neural Networks. Sensors (Basel, Switzerland), Vol.18, pp. 2110-2124.

[20] Zhang, K. , Xu, J. , Min, M. , Jiang, G. , Pelechrinis, K. , Zhang, H. (2016). Automated IT System Failure Prediction: A Deep Learning Approach. In IEEE International Conference on Big Data, pp. 1291-1300.

[21] Gyftakis, K. N., Spyropoulos, D. V., Kappatou, J. C., Mitronikas, E. D. (2013). A Novel Approach for Broken Bar Fault Diagnosis in Induction Motors Through Torque Monitoring, In IEEE Transactions on Energy Conversion, Vol. 28, pp. 267-277.

[22] Olah, C. (2015). Understanding LSTM Networks:
http://colah.github.io/posts/2015-08-Understanding-LSTMs/

指導教授

陳振明(Jen-Ming Chen)

審核日期

2020-7-3

推文