應用GAN模型生成設備運轉資料結合LSTM模型於預診斷與健康管理之異常訊號偵測

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：101

、訪客IP：52.15.185.147

姓名

盧盈穎(Yin-Ying Lu) 查詢紙本館藏

畢業系所

工業管理研究所

論文名稱

應用GAN模型生成設備運轉資料結合LSTM模型於預診斷與健康管理之異常訊號偵測
(Applications of GAN Models to Generate Operation Data of Equipment Combined with LSTM Models to Detect Abnormal Signals in Prognostics and Health Management)

相關論文

★ 應用灰色理論於有機農產品之經營管理— 需求預測及關鍵成功因素探討	★ NAND型Flash價格與交運量預測在風險分析下之決策模式
★ 工業電腦用無鉛晶片組最適存貨政策之研究-以A公司為例	★ 砷化鎵代工廠磊晶之最適存貨管理-以W公司為例
★ 資訊分享&決策制定下產銷協同關係之研究 -以IC設計業為例	★ 應用分析層級法於電子化學品業委外供應商評選準則之研究
★ 應用資料探勘於汽車售服零件庫存滯銷因素分析-以C公司為例	★ 多目標規劃最佳六標準差水準: 以薄膜電晶體液晶顯示器C公司製造流程為例
★ 以資料探勘技術進行消費者返廠定期保養之實證研究	★ 以價值鏈觀點探討品牌公司關鍵組織流程之取決-以S公司為例
★ 應用產銷協同規劃之流程改善於化纖產業-現況改善與效益分析	★ 權力模式與合作關係對於報價策略之影響研究—以半導體產業A公司為例
★ 應用資料探勘於汽車製造業之庫存原因分析	★ 以類神經網路預測代工費報價---以中小面板產業C公司為例
★ 電路板產業存貨改善研究-以N公司為例	★ 運用六標準差改善機台備用零件(Spare parts)存貨管理

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

隨著工業4.0的興起，製造業透過傳感器收集設備於生產流程上的資料，並利用這些資料建立模型及分析，促使他們可以朝向智慧工廠進行轉型。運用機器學習的方式分析收集數據達到預測設備故障或停機時間及原因，同時也希望透過制定有效的管理策略，來進行後續的設備維護。因此衍伸出預診斷與健康管理(PHM)。期望透過此模型使設備維護更有效率，為工廠提供更靈活、適應性更強的製造模式，不會因為設備臨時停機導致生產流程出現缺口。
本研究欲使用A公司所提供之塗佈機生產數據，運用預診斷與健康管理為架構改善該公司原有之維護策略。透過生成對抗網路(GAN)生成異常訊號，並於長短期記憶網路(LSTM)建置預測模型。依照異常徵兆分布狀況分別建置三種不同時間段模型比較模型間之績效，分別為5分鐘模型、15分鐘模型及25分鐘模型。最終選定15分鐘模型作為LSTM設備監控模型，判讀績效為準確率為97.56%、精確率為100%、召回率為93.33%及F_1-Score為96.55%。將其模型應用於塗佈機捲取B軸之運行狀態監控，LSTM監控模型在此次實驗中針對異常訊號會發出兩次異常預警報，第一次預警報提早約30分鐘發出，第二次預警報提早約1分鐘發出。因此，本研究針對這兩次預警報制訂一項判讀異常之規則，並制訂預診斷與健康管理維護策略於捲取B軸。

摘要(英)

With the emergence of Industry 4.0, the manufacturing industry has begun to collect data from machines in the production process through sensors. They use this data to transition towards smart factories, analyzing the collected data through machine learning to predict equipment failures or downtime and their causes. Simultaneously, there is a desire to develop effective management strategies for subsequent equipment maintenance. This has led to Prognostics and Health Management (PHM), aiming to make equipment maintenance more efficient. The goal is to provide manufacturers with a more flexible and adaptive manufacturing model while pursuing improved production quality, processes, and rapid responses to changes in production to meet customer demands. This approach ensures that temporary equipment shutdowns do not lead to gaps in the production process.
In this study, we aim to utilize production data from Company A′s coating machine and improve its existing maintenance strategy using the Prognostics and Health Management framework. We will apply Generative Adversarial Network (GAN) to generate abnormal signals and build a prediction model with Long Short-Term Memory (LSTM). According to the distribution of abnormal symptom, the research compares the performance of three different time-segment models, namely the 5-minute model, the 15-minute model, and the 25-minute model. This study selects the 15-minute model as the LSTM device monitoring model, achieving an accuracy rate of 97.56%, precision rate of 100%, recall rate of 93.33%, and F_1-Score of 96.55%.
Subsequently, this model is applied to monitor the operation status of the winder B in the coating machine, issuing two early warnings for anomalies. The first warning is issued approximately 30 minutes in advance, while the second warning is issued approximately 1 minute in advance. ased on these warnings, the research proposes a rule for judging anomalies and establishes a Prognostics and Health Management maintenance strategy for the winder B in the coating machine.

關鍵字(中)

★ 預診斷與健康管理
★ 異常檢測
★ 生成對抗網路
★ 長短期記憶網路

關鍵字(英)

★ Prognostics and Health Management
★ Anomaly Detection
★ Generative Adversarial Network
★ Long Short-Term Memory Network

論文目次

中文摘要 i
ABSTRACT ii
目錄 iii
圖目錄 v
表目錄 vii
一、緒論 1
1.1　研究背景 1
1.2　研究動機 2
1.3　研究目的 2
1.4　研究假設及限制 3
1.5　研究流程 3
二、文獻回顧與探討 6
2.1　維護策略 6
2.1.1　預測性維護 7
2.1.2　預診斷與健康管理 8
2.2　預診斷與健康管理的預測方法 10
2.2.1　機器學習 10
2.2.2　深度學習 11
2.3　文獻回顧與探討小結 16
三、研究方法 17
3.1　研究對象及研究架構 17
3.2　資料集介紹 20
3.3　資料前處理 21
3.3.1　異常訊號擷取 21
3.3.2　資料標準化 28
3.4　異常訊號分析 28
3.4.1　生成對抗網路 28
3.4.2　長短期記憶網路 32
3.5　評估指標 34
四、實驗結果與分析 36
4.1　實驗環境與設備介紹 36
4.2　GAN模型績效評估 36
4.3　LSTM模型績效評估 41
4.3.1　實驗樣本 41
4.3.2　LSTM各時間模型績效評估 42
五、討論 48
5.1　應用架構設計 48
5.2　應用分析及結果 49
5.3　判讀異常規則 51
5.4　制定維護策略 51
六、結論與建議 53
6.1　結論 53
6.2　未來研究建議 54
七、參考文獻 55

參考文獻

[1] Abidi, M. H., Mohammed, M. K., & Alkhalefah, H. (2022). Predictive Maintenance Planning for Industry 4.0 Using Machine Learning for Sustainable Manufacturing. Sustainability, 14(6).
[2] Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., Hasan, M., Van Essen, B. C., Awwal, A. A. S., & Asari, V. K. (2019). A State-of-the-Art Survey on Deep Learning Theory and Architectures. Electronics, 8(3).
[3] Balasch, A., Beinhofer, M., & Zauner, G. (2020). The Relative Confusion Matrix, a Tool to Assess Classifiablility in Large Scale Picking Applications. 2020 IEEE International Conference on Robotics and Automation (ICRA)
[4] Castelo-Branco, I., Oliveira, T., Simões-Coelho, P., Portugal, J., & Filipe, I. (2022). Measuring the fourth industrial revolution through the Industry 4.0 lens: The relevance of resources, capabilities and the value chain. Computers in Industry, 138.
[5] Çınar, Z. M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0. Sustainability, 12(19).
[6] Compare, M., Baraldi, P., & Zio, E. (2020). Challenges to IoT-Enabled Predictive Maintenance for Industry 4.0. IEEE Internet of Things Journal, 7(5), 4585-4597.
[7] Decelle, A. (2023). An Introduction to Machine Learning: a perspective from Statistical Physics. Physica A: Statistical Mechanics and its Applications, 631.
[8] Fila, R., Khaili, M. E., & Mestari, M. (2020). Cloud Computing for Industrial Predictive Maintenance Based on Prognostics and Health Management. Procedia Computer Science, 177, 631-638.
[9] Fink, O., Wang, Q., Svensén, M., Dersin, P., Lee, W.-J., & Ducoffe, M. (2020). Potential, challenges and future directions for deep learning in prognostics and health management applications. Engineering Applications of Artificial Intelligence, 92.
[10] Florian, E., Sgarbossa, F., & Zennaro, I. (2021). Machine learning-based predictive maintenance: A cost-oriented model for implementation. International Journal of Production Economics, 236.
[11] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27.
[12] Huang, J., Chang, Q., & Arinez, J. (2020). Deep reinforcement learning based preventive maintenance policy for serial production lines. Expert Systems with Applications, 160.
[13] Islam, S., Elmekki, H., Elsebai, A., Bentahar, J., Drawel, N., Rjoub, G., & Pedrycz, W. (2024). A comprehensive survey on applications of transformers for deep learning tasks. Expert Systems with Applications, 241, 122666.
[14] Kerkhof, M., Wu, L., Perin, G., & Picek, S. (2023). No (good) loss no gain: systematic evaluation of loss functions in deep learning-based side-channel analysis. Journal of Cryptographic Engineering, 13(3), 311-324.
[15] Khan, S., & Yairi, T. (2018). A review on the application of deep learning in system health management. Mechanical Systems and Signal Processing, 107, 241-265.
[16] 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.
[17] Maria Navin, J., & Pankaja, R. (2016). Performance analysis of text classification algorithms using confusion matrix. International Journal of Engineering and Technical Research (IJETR), 6(4), 75-78.
[18] Masum, S., Liu, Y., & Chiverton, J. (2018). Multi-step Time Series Forecasting of Electric Load Using Machine Learning Models. In Artificial Intelligence and Soft Computing (pp. 148-159).
[19] Mercioni, M. A., & Holban, S. (2020). The Most Used Activation Functions: Classic Versus Current. 2020 International Conference on Development and Application Systems (DAS).
[20] Nguyen, H. D., Tran, K. P., Thomassey, S., & Hamad, M. (2021). Forecasting and Anomaly Detection approaches using LSTM and LSTM Autoencoder techniques with the applications in supply chain management. International Journal of Information Management, 57.
[21] Pech, M., Vrchota, J., & Bednar, J. (2021). Predictive Maintenance and Intelligent Sensors in Smart Factory: Review. Sensors (Basel), 21(4).
[22] Roque, A. S., Krebs, V. W., Figueiro, I. C., & Jazdi, N. (2022). An analysis of machine learning algorithms in rotating machines maintenance. IFAC-PapersOnLine, 55(2), 252-257.
[23] Shao, M., & Gu, N. (2021). Anomaly Detection Algorithm Based on Semi-Supervised Collaborative Strategy. Journal of Physics: Conference Series, 1944(1), 012017.
[24] Sharma, D. B., Sripradha, Nikita, Kodipalli, A., Rao, T., & R, B. R. (2023). Machine Predictive Maintenance Classification Using Machine Learning. 2023 International Conference on Computational Intelligence for Information, Security and Communication Applications (CIISCA).
[25] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
[26] Vogl, G. W., Weiss, B. A., & Helu, M. (2019). A review of diagnostic and prognostic capabilities and best practices for manufacturing. J Intell Manuf, 30(1), 79-95.
[27] Xu, P. (2019). Review on Studies of Machine Learning Algorithms. Journal of Physics: Conference Series, 1187(5), 052103.
[28] Yang, G., Ma, Q., Sun, H., & Zhang, X. (2022). State of Health Estimation Based on GAN-LSTM-TL for Lithium-ion Batteries. International Journal of Electrochemical Science, 17(11).
[29] Zonta, T., da Costa, C. A., da Rosa Righi, R., de Lima, M. J., da Trindade, E. S., & Li, G. P. (2020). Predictive maintenance in the Industry 4.0: A systematic literature review. Computers & Industrial Engineering, 150.

指導教授

陳振明(Jen-Ming Chen)

審核日期

2024-7-1

推文